AI Ethics

A Beginner’s Guide to Emotional AI, Its Challenges and Opportunities

You walk into your living room, Alexa dims the lights, lowers the temperature, and says: “You look really sad today. Would you like me to play Adele for you?” This could be a reality in a few years. Are we prepared? This beginner’s guide to emotional AI will introduce the technology, its applications and ethical challenges.

We will explore both the opportunities and dangers of this emerging AI application. This is part of the broader discipline of affective computing that use different inputs from the human body (i.e.: heartbeat, sweat, facial expression, speech, eye movement, etc) to interpret, emulate and predict emotion. For this piece, we’ll focus on the use of facial expressions to infer emotion.

According to Gartner, by 2022, 10% of smartphones will have affective computing capabilities. Latest Apple phones can already detect your identity through your face. The next step is detecting your mental state through that front camera. Estimates of the Emotive AI market range around $36 Billion in 5 years. Human emotion detection is no longer a Sci-Fi pipe dream but a reality poised to transform societies. Are we ready for it?

How does Emotional AI work?

Our beginner’s guide to emotional AI must start with explaining how it works. While this technology is relatively new, its foundation dates back to the mid-nineteenth century. founded primarily in the idea humans display universal facial cues for their emotions. Charles Darwin was one of the first to put forth this idea. A century later, American psychologist Paul Eckman further elaborated on it through extensive field studies. Recently, scholars have challenged this universality and there is now no consensus on its validity. AI entrepreneurs bet that we can find universal patterns. Their endeavors are testing this theory in real-time with machine learning.

The first step is “training” a computer to read emotions through a process of supervised learning. This entails feeding pictures of people’s faces along with labels that define that person’s emotion. For example, one could feed the picture of someone smiling with the label “happy.” For the learning process to be effective thousands if not millions of these examples are created.

The computer then uses machine learning algorithms to detect common patterns in the many examples of each emotion. This enables it to establish a general idea of what each emotion looks like in every face. Therefore, it is able to classify new cases, including faces it has never encountered before, with these emotions.

By Prawny from Pixabay

Commercial and Public Applications

As you can imagine, there are manifold applications to this type of technology. For example, one of the greatest challenges in marketing is collecting accurate feedback from customers. Satisfaction surveys are few and far between and often inaccurate. Hence, companies could use emotional AI to capture instantaneous human reactions to an ad or experience not from a survey but from their facial reactions.

Affectiva, a leading company using this technology, already claims it can detect emotions from any face. It collected 10M expressions from 87 countries, hand-labeled by crowd workers from Cairo. With its recent merger with Smart Eye, the company is poised to become the leader in Automotive, in-cabin driver mental state recognition for the automotive industry. This could be a valuable safety feature detecting when a driver is under the influence, sleepy, or in emotional distress.

More controversial applications include using it for surveillance as it is in the case of China’s treatment of the Uighur population. Police departments could use it as a lie-detection device in interrogations. Governments could use it to track the general mood of the population by scanning faces in the public square. Finally, employers could use it as part of the interview process to measure the mental state of an applicant.

Ethical Challenges for Emotional AI

No beginner’s guide on emotional AI without considering the ethics of its impact. Kate Crawford, a USC research professor, has sounded the alarm on emotional AI. In her recently published book and through a number of articles, she makes the case for regulating this technology. Her primary argument is that using facial recognition to detect emotions is based on shaky science. That is, the overriding premise that human emotion can universally be categorized through a set of facial expressions is faulty. It minimizes a plethora of cultural factors lending itself to dangerous bias.

This is not just conjecture, a recent University of Maryland study detected an inherent bias that tends to show black faces with more negative emotions than white faces. She also notes that the machine learning process is questionable as it is based on pictures of humans emulating emotions. The examples come from people that were told to make a type of facial expression as opposed to capturing a real reaction. This can lead to an artificial establishment of what a facial representation of emotion should look like rather than real emotional displays.

This is not limited to emotion detection. Instead it is part of a broader partner of error in facial recognition. In 2018 paper, MIT researcher Joy Bulowanmini analyzed disparities in the effectiveness of commercial facial recognition applications. She found that misclassification rates for dark-skinned women were up to 34% compared to 0.8% for white males.

The Sanctity of the Human Face

The face is the window to the human soul. It the part of the body most identified with an individual’s unique identity. When we remember someone, it is their countenance that shows up in our mind. It is indeed the best indicator of our internal emotional state which may often betray the very things we speak.

Up to recently, interpreting the mystery of our faces was the job of humans and animals. What does it mean to now have machines that can intelligently decipher our inner states? This is certainly a new frontier in the human-ai interface that we must tread carefully if for no other reason than to respect the sanctity of the human soul. If left for commerce to decide, the process will most likely be faster than we as a society are comfortable with. That is where the calls for regulation are spot on.

Like every technology – the devil is in the details. It would be premature to outlaw the practice altogether as the city of Seattle has done recently. We should, however, limit and monitor its uses – especially in areas where the risk of bias can have a severe adverse impact on the individual. We must also ponder whether we want to live in a society where even our facial expressions are subject to monitoring.

Netflix “Oxygen”: Life, Technology and Hope French Style

I am hesitant to watch French movies as the protagonist often dies at the end. Would this be another case of learning to love the main character only to see her die at the end? Given the movie premise, it was worth the risk. Similar to Eden, Netflix Oxygen is a powerful exploration of the intersection of hope and technology.

It is uncommon to see a French movie make it to the top charts of American audiences. Given our royal laziness, we tend to stay away from anything that has subtitles preferring more the glorified-theatrics-simplistic plots of Hollywood. The French are too sophisticated for that. For them, movies are not entertainment but an art form.

Realizing I had never watched a French Sci-Fi Thriller, maybe it was time to walk down that road. I am glad I did. The next day, I reflected on the movie’s plot after re-telling the whole story to my wife and my daughter. Following the instigating conversation that ensued, I realized there was enough material for an AI theology review.

Simple Plot of Human AI Partnership

You wake up and find yourself trapped in a capsule. You knock on the walls eventually activating an AI that informs you that you are in a cryogenic chamber. There is no way of knowing how you got there and how you can get out. You have 90 minutes before the oxygen runs out. The clock is ticking and you need to find a way to survive or simply accept your untimely death.

Slowly the main character played by Melanie Laurent, Elizabeth, discovers pieces and puzzles about who she is, why she is in the chamber and ultimately what options she has. This journey is punctuated by painful discoveries and a few close calls building the suspense through out the feature.

Her only companion throughout this ordeal is the chamber AI voice assistant, Milo. She converses, argues and pleads with him through out as she struggles to find a way to survive. The movie revolves around their unexpected partnership, as the AI is her only way to learn about her past and communicate with the outside world. The contrast between his calm monotone voice with her desperate cries further energize the movie’s dramatic effect.

In my view, the plot’s simple premise along with Melanie’s superb performance makes the movie work even as it stays centered on one life and one location the whole time.

Spoiler Alert: The next sections give away key parts of the plot.

AI Ethics, Cryogenics and Space Travel

Oxygen is the type of film that you wake up the next day thinking about it. That is, the impact is not clearly felt until later. There is so much to process that its meaning does not become clear right away. The viewer is so involved in the main character’s ordeal that you don’t have time to reflect on the major ethical, philosophical and theological issues that emerge in the story.

For example, once Elizabeth wakes up, one of the first things Milo offers her is sedatives. She refuses, preferring to be alert in her struggle for survival rather than calmly accepting her slow death. In one of the most dramatic scenes of the movie, Milo follows protocol to euthanize her as she is reaching the end of her oxygen supply. In an ironic twist that Elizabeth picks up on: the AI asks her permission for sedatives but does not consult her about the ultimate decision to end her life. While a work of fiction, this may very well be sign of things to come, as assisted suicide becomes legal in many parts of the world. Is it assisted-suicide of humane end-of-life care?

In an interesting combination, Oxygen portrays cryogenics, cloning and space travel as the ultimate solution for human survival. As humanity faced a growing host of incurable diseases they send a spaceship with thousands of clones in cryogenic chambers to find the cure in another planet. Elizabeth, as she learns mid-way, is a clone of a famous cryogenics scientist carrying her memories and DNA. This certainly raises interesting questions about the singularity of the human soul. Can it really transfer to clones or are they altogether different beings? Is memory and DNA the totality of our beings or are there transcending parts impossible to replicate in a lab?

Photo by Joshua Earle on Unsplash

Co-Creating Hopeful Futures

In the end, human ingenuity prevails. Through a series of discoveries, Liz finds a way to survive. It entails asking Milo to transfer the oxygen from other cryogenic chambers into hers. Her untimely awakening was the result of an asteroid collision that affected a number of other chambers. After ensuring there were no other survivors in these damaged chambers, she asks for the oxygen transfer.

To my surprise, the movie turns out to be a colossal affirmation of life. Where the flourishing of life is, there is also theology. While having no religious content, the story shows how the love for self and others can lead us to fight for life. Liz learns that her husband’s clone is in the spaceship which gives her a reason to go on. This stays true even after she learns she herself is a clone and in effect have never met or lived with him. The memory of their life together is enough to propel her forward, overcoming insurmountable odds to stay alive.

The story also illustrates the power of augmentation, how humans enabled through technology can find innovative solutions that extend life. In that sense, the movie aligns with a Christian Transhumanist view – one that sees humans co-creating hopeful futures with the divine.

Even if God is not present explicitly, the divine seems to whisper through Milo’s reassuring voice.

Citizens Unite: Global Efforts to Stand Up to Digital Monopolies

Politicians lack the knowledge to regulate technology. This was comically demonstrated in 2018 when Senator Hatch asked how Zuckerberg could keep Facebook free. Zuckerberg’s response became a viral meme:

Taken from Tenor.com

Zuckerberg’s creepy smile aside, the meme drives home the point that politicians know little about emerging technologies.

What can be done about this? Lawmakers cannot be experts on everything – they need good counsel. An example of that is how challenging it was for the governments to contain COVID with no help from microbiologists or researchers. The way we get to good policy is by having expert regulators who act as referees, weighing the pros and cons of different strategies to help the lawmakers deliberate with at least some knowledge.

A Global Push to Fight Digital Monopolies

When we take a look at monopolies around the world, it’s clear that digital monopolies are everywhere, and alongside them are the finance companies and banks. We live in a capitalist world. Technology walks holding hands with the urge to profit and make money. That is why it is so hard to go against these monopolies.

But not all hope is lost. If we look across the globe, we can find different countries regulating big tech companies. Australia has been working for more than a year now, proposing a legislation that would force tech platforms like Google and Facebook to pay news publishers for content. The tension was so big that Facebook took an extreme measure and blocked all kinds of news in Australia. The government thinks that Facebook’s news ban was too aggressive and will only push the community even more further from Facebook.

The Australian Prime Minister Scott Morrison, shared on his Facebook page his concerns and beliefs saying that this behavior from Facebook only shows how these Big Tech Companies think they are bigger than the government itself and that rules should not apply to them. He also says that he recognizes how big tech companies are changing the world, but that does not mean they run it.

Discussions on how to stop big companies using every content for free is also happening in other countries like France, Canada and even the United States. Governments around the world are considering new laws to keep these companies in check. The question is how far they can go against the biggest digital monopolies in the world.

Fortunately, there are many examples where governments are working with tech companies to help consumers. Early this year, the French government approved the New Tech Repairability Index. This index is going to show how repairable an electronic is, like smartphones, laptops, TVs, and even lawnmowers. This will help consumers buy more durable goods and force companies to make repairs possible. It is not only a consumer-friendly measure but also environmentally friendly as it helps reduce electronic waste.

Another example that big technology companies have to hear from the government is in Brazil. On February 16, a Brazilian congressman was arrested for making and sharing videos that go against the law by uplifting a very dark moment in Brazilian history, the military dictatorship they had to go through in the 60s. And a few days later, Facebook, Twitter, and Instagram had to ban his accounts because of a court order, since he was still updating his account from inside prison.

Brazil still doesn’t know how this congressman’s story will end, but we can at least hope that the cooperation between big companies and the government will increase even more. These laws and actions by the people in charge of countries have already waited too long to come along. We have to fight for our rights and always remember that no one is above the law.

From Consumers to Citizens

Technological monopolies can make us feel like they rule the world. But the truth is that we are the consumers, so we need to have our voices heard and rights respected.

I believe that the most efficient way to deal with tech monopolies is by creating committees that will assist the government to make antitrust laws. These committees should have experts and common citizens that don’t have any ties with big tech companies. Antitrust laws are statutes developed by governments to protect consumers from predatory business practices and ensure fair competition. They basically ensure companies don’t have questionable activities like market allocation, bid rigging, price-fixing, and the creation of monopolies.

Protecting consumer privacy and deterring artificially high prices should be a priority. But can these committees really be impartial? Can we trust the government to make these laws?

The only way is for consumers to act as citizens. That is, we need to vote for representatives that are not tied to Big Tech lobbies. We need to make smarter choices with our purchases. We need to organize interest groups that put humanity back at the center of technology.

How are you standing up to digital monopolies today?

Surveillance Capitalism: Exposing the Power of Digital Monopolies

On January 28, I attended the online forum Medium in Conversation: How to Destroy Surveillance Capitalism. In this blog, I summarize the main points from the discussion along with some reflections on how we can respond.

Maybe at first glance, we can’t really see what surveillance capitalism has to do with AI. But the two topics walk side by side. Surveillance capitalism is sustained by digital monopolies that rely on massive amounts of personal data (hence the surveillance part). This deluge of data is fed into powerful AI algorithms which drive content curation. One depends on the other to thrive.

The Current State of Affairs

It’s a new era for Big Tech. Weeks after the de-platforming of Donald Trump—and with a new administration in the White House—the time is ripe to reexamine the power wielded by the giants of surveillance capitalism. How did corporations like Facebook, Google, and Amazon amass such power? How do we build a more open Web?

According to Cory Doctorow, If we´re going to break big techs’ dominance in our digital lives, we will have to fight monopolies. That may sound pretty mundane and old-fashioned, something out of the new deal era. Yet, breaking up monopolies is something we have forgotten how to do. The trust-busting era cannot begin until we find the political will. Only when politicians prove that they have the average citizen’s backs against the richest most powerful men in the world.

For politicians to take notice, citizens must first speak up.

What is the problem with Monopolies?

In case we need a refresher, monopoly is a bad deal for consumers. It means that the market has only one seller with the ability to set prices, and tell people what a service costs. People line up to buy their product even if it costs too much simply because they have no choice.

Facebook is a monopoly if you think of the prices it set for its ad platform. The ad buyer has very little choice allowing Zuckerberg’s empire to dictate the terms. In addition to that, the platform behemoth retains its monopoly by impeding other apps to grow.

Anticompetitive conduct in big tech has been rampant. Mark Zuckerberg bought competing apps (snapchat, instagram for example) leaving little room for competitors. Apple pursued it in the hardware side by shutting down “right to repair bills” so that people are forced to buy new phones. In effect, they dictated when your phone can be repaired or when it has to be thrown away.

These actions led to an unprecedented concentration of power where a small group of people can make decisions of global consequence.

People of the World, Unite

Is it a realistic operation to create an open web or are we too far gone? Although these forces seem impenetrable and timeless, they actually are relatively new, and have weaknesses. If it was about just changing our relationship with technology, it would be a hard lift.

Yet, according to Cory Doctorow, there is a wave sweeping the world with anger about monopolies in every domain. This discontent seek to return power to communities so they can decide their future.

It has been done before. In the beginning of the 20th century, popular discontent drove politicians to rein in powerful monopolies such as Andrew Carneggie’s control of the steel industry and Rockefeller’s Oil’s monopoly. Their efforts culminated with the passage of sweeping anti-trust legislation.

Are we reaching a tipping point with big tech in the beginning of the 21st century?

Conclusion

Surveillance Capitalism affects the entire world and can be scary sometimes. There is a need to seek freedom from the domain of digital monopolies. Once again, it is necessary to find the political will to fight for change. While legislation will not solve this problem completely, it is an important first step.

Certainly this is not just a North American problem. Some countries are already pressing these big companies to answer for their actions paving the way for a future where power is more evenly distributed.

In the next blog, I’ll provide an overview of anti-trust efforts around the world.

Green Tech: How Scientists are Using AI to Fight Deforestation

In the previous blog, I talked about upcoming changes to US AI policy with a new administration. Part of that change is a renewed focus on harnessing this technology for sustainability. Here I will showcase an example of green tech – how machine learning models are helping researchers detect illegal logging and burning in the vast Amazon rainforest. This is an exciting development and one more example of how AI can work for good.

The problem

Imagining trying to patrol an area nearly the size of the lower 48 states of dense rainforest! It is as the proverbial saying goes: finding needle in a haystack. The only way to to catch illegal activity is to find ways to narrow the surveilling area. Doing so gives you the best chances to use your limited resources of law enforcement wisely. Yet, how can that be done?

How do illegal logging and burning happen in the Amazon? Are there any patterns that could help narrow the search? Fortunately, there is. A common trait for them is happening near a road. In fact, 95% of them occur within 6 miles from a road or a river. These activities require equipment that must be transported through dense jungle. For logging, lumber must be transported so it can be traded. The only way to do that is either through waterways or dirt roads. Hence, tracking and locating illegal roads go along way to honing in areas of possible illegal activity.

While authorities had records for the government-built roads, no one knew the extent of the illegal network of roads in the Amazon. To attack the problem, enforcing agencies needed richer maps that could spot this unofficial web. Only then could they start to focus resources around these roads. Voila, there you have, green tech working for preserving rather than destroying the environment.

An Ingenious solution

In order to solve this problem, Scientist from Imazon (Amazon’s Institute of Humans and the Environment) went to work in a search for ways to detect these roads. Fortunately, by carefully studying satellite imagery they could manually trace these additional roads. In 2016 they completed this initial heroic but rather tedious work. The new estimate was now 13 times the size of the original! Now they had something to work with.

Once the initial tracing was complete, it became clear updating it manually would be an impossible task. These roads could spring up overnight as loggers and ranchers worked to evade monitoring. That is when they turned to computer vision to see if it could detect new roads. The initial manual work became the training dataset that taught the algorithm how to detect these roads from the satellite images. In supervised learning, one must first have a collection of data that shows the actual target (labels) to the algorithm (i.e: an algorithm to recognize cats must first be fed with millions of Youtube videos of cats to work).

The result was impressive. At first, the model achieved 70% accuracy and with some additional processing on top, it increased to 90%. The research team presented their results in the latest meeting of the American Geophysical Union. They also plan to share their model with neighboring countries so they can use it for their enforcement of the Amazon in areas outside Brazil.

Reflection

Algorithms can be effective allies in the fight for preserving the environment. As the example of Imazon shows, it takes some ingenuity, hard work, and planning to make that happen. While a lot of discussions around AI quickly devolve into cliches of “machines replacing humans”, this example shows how it can augment human problem-solving abilities. It took a person to connect the dots between the potential of AI for solving a particular problem. Indeed the real future of AI may be in green tech.

In this blog and in our FB community we seek to challenge, question and re-imagine how technologies like AI can empower human flourishing. Yet, this is not limited to humans but to the whole ecosystem we inhabit. If algorithms are to fulfill their promise, then they must be relevant in sustainability.

How is your work making life more sustainable on this planet?

5 Changes the Biden-Harris Administration will Bring to AI Policy

As a new administration takes the reins of the federal government, there is a lot of speculation as to how they will steer policy in the area of technology and innovation. This issue is even more relevant as social media giants grapple with free speech in their platforms, Google is struggles with AI ethics and concerns over video surveillance grows. In the global stage, China moves forward with its ambitions of AI dominance and Europe continues to grapple with issues of data governance and privacy.

In this scenario, what will a Biden-Harris administration mean for AI in the US and global stage? In a previous blog, I described the decentralized US AI strategy, mainly driven by large corporations in Silicon Valley. Will a Biden administration bring continuity to this trend or will it change direction? While it is early to say for sure, we should expect 5 shifts as outlined below:

(1) Increased investment in non-military AI applications: In contrast to the $2 Bi promised by the Trump White House, Biden plans to ramp up public investment in R&D for AI and other emerging technologies. Official campaign statements promise a whopping $300 billion of investment. This is a significant change since public research funds tend to aim at socially conscious applications rather than profit-seeking ventures preferred by private investment. These investments should steer innovation towards social goals such as climate change, revitalizing the economy, and expanding opportunity. In the education front, $5 billion is earmarked for graduate programs in teaching STEM. These are important steps as nations across the globe seek to gain the upper hand on this crucial technology.

(2) Stricter bans on facial recognition: While this is mostly speculation at this point, industry observers cite Kamala’s recent statements and actions as an indication of forthcoming stricter rules. In her plan to reform the justice system, she cites concerns with law enforcement’s use of facial recognition and surveillance. In 2018, she sent letters to federal agencies urging them to take a closer look at the use of facial recognition in their practices as well as the industries they oversee. This keen interest in this AI application could eventually translate into strong legislation to regulate, curtail or even ban the use of facial recognition. It will probably fall somewhere between Europe’s 5-year ban on it and China’s pervasive use to keep the population in check.

Photo by ThisisEngineering RAEng on Unsplash

(3) Renewed anti-trust push on Big Tech: The recent move started by Trump administration to challenge the big tech oligarchy should intensify under the new administration. Considering that the “FAMG”(Facebook, Amazon, Microsoft, and Google) group is in the avant-garde of AI innovation, any disruption to their business structures could impact advances in this area. Yet, a more competitive tech industry could also mean an increase in innovation. It is hard to determine how this will ultimately impact AI development in the US but it is a trend to watch in the next few years.

(4) Increased regulation: It is likely but not certain at this point. Every time a Democratic administration takes power, the underlying assumption by Wall Street is that regulation will increase. Compared to the previous administration’s appetite for dismantling regulation, the Biden presidency will certainly be a change. Yet, it remains to be seen how they will go about in the area of technology. Will they listen to experts and put science in front of politics? AI will definitely be a test of it. They will certainly see government as a strong partner with private industry. Also, they will likely walk back Trump’s tax cuts on business which could hamper innovations for some players.

(5) Greater involvement in the global stage: the Biden administration is likely to work closer with allies, especially in Europe. Obama’s AI principles released in 2012 became a starting point for the vigorous regulatory efforts that arose in Europe in the last 5 years. It would be great to see increased collaboration that would help the US establish strong privacy safeguards as the ones outlined by the GDPR. In regards to China, Biden will probably be more assertive than Obama but less belligerent than Trump. This could translate into restricting access to key technologies and holding China’s feet to the fire on surveillance abuses.

The challenges in this area are immense requiring careful analysis and deliberation. Brash decisions based on ideological short-cuts can both hamper innovation and fail to safeguard privacy. It is also important to build a nimble apparatus that can respond to the evolving nature of this technology. While not as urgent as COVID and the economy, the federal government cannot afford to delay reforming regulation for AI. Ethical concerns and privacy protection should be at the forefront seconded by incentives for social innovation.

Preparing for a Post-COVID-19 AI-driven Workplace

Are we ready for the change this pandemic will bring? Are we ready to encounter the accelerating threats to the workplace that were envisioned only years ahead? What can this pandemic teach us about being useful in the future where AI will continue to re-arrange the workplace?

Sign of Things to Come

As the coronavirus was spreading rapidly through Japan in March, workers in Sugito found a spiking sudden demand for hygiene products such as masks, hand sanitizers, gloves, and medical protection supplies. To reduce the danger of contamination, the company that operates the center, Paltac, is engaging in a revolutionary idea. They are not just considering, but are already initiating hiring robots to replace human manufacturing, at least until social distancing is no longer needed.

“Robots are just one tool for adapting to the new normal.” Says Will Knight, senior writer for WIRED, in his article where he evaluates the Japanese pandemic situation, and how manufacturing Japanese companies are dealing with social distancing.

Some think that this is an unmatched opportunity to adapt and deliver in the AI community. Especially medical Robo tech – if they had been sought out more thoroughly beforehand, maybe the present outcome wouldn’t have been so catastrophic. Science journalist Matt Simon illustrates this in his article, and reassures that: “Evermore sophisticated robots and AI are augmenting human workers”

The greater question is will AI replace or augment workers? Our future may depend on the answer to this question.

A Bigger Threat than a Virus?

In 2016 Harvard scientists released a study on “12 risks that threaten human civilizatio n.” In it, they, not only outline the risks but also show ways that we can prepare for them. Prophetically, the study cites a global pandemic at the top of the list. It correctly classified it as “more likely than assumed” and they could not have been more correct. We now wish global leaders had heeded their warnings.

What other risks does the study warn us about? The scientists consider Artificial Intelligence as one of the major, but unfortunately the least of all comprehended global risks. In spite of its limitless potential, there is a grave risk of such intelligence developing into something uncontrollable.

It is not just a probability, but a questionable enigma of when. It could bring significant economic disruption, predicting that AI could copy and surpass human proficiency in speed and performance. While current technology is nowhere near this scenario, the mere possibility of this predicament should cause us to pause for reflection.

Yet, even as this pandemic has shown, the greatest threats are also the biggest opportunities for doing good in the world.

Learning to Face the Unknown

Our very survival depends on our ability to stay awake, to adjust to new ideas, to remain vigilant and to face the challenge of change.
Martin Luther King Jr.

Change is inevitable. Whether coming by exquisite and unique technology or a deadly virus, it will eventually disrupt our ideal routines. The difference is in how we position ourselves to face these adversities alongside those who we love and are responsible for. If humans can correctly predict tragedies, how much more can we do to avoid them!

The key to the future is the ability to adapt in the face of change. People that only react to what is “predictable” will be replaced by robots or algorithms. For example, as a teacher, I studied many things but never thought that I would have to become a Youtuber. No one ever taught me about the systems to help me access via the internet. I was not trained for this! Yet, because of this pandemic, I now have to teach through creating videos and uploading them online. I am learning to become a worker of the future.

May we use this quarantined year as an incubating opportunity to prepare ourselves for a world that will not be the same. May we train ourselves to endure challenges, and also to see the opportunities that lie in plain sight. This is my hope and prayer for all of you.

STAY HOME, STAY SAFE, STAY SANE

AI Impact on Jobs: How can Workers Prepare?

In a previous blog, I explored the main findings from a recent MIT paper on AI’s impact on work. In this blog, I want to offer practical advice for workers worried about their jobs future. There is a lot automation anxiety surrounding the topic which often gets amplified through click-bait sensational articles. Fortunately, the research from the MIT-IBM Watson paper offers sensible and detailed enough information to help workers take charge of their careers. Here are the main highlights.

From Jobs to Tasks

The first important learning from the report is to think of your job as group of tasks rather than a homogenous unit. The average worker performs a wide range of tasks from communicating issues, solving problems, selling ideas to evaluating others. If you never thought of your job this way, here is a suggestion: track what you do in one work day. Pay attention to the different tasks you perform and write down the time it takes to complete them. Be specific enough in descriptions that go beyond “checking emails.” When you read and write emails, you are trying to accomplish something. What is it?

Once you do that for a few days, you start getting a clearer picture of your job as a collection of tasks. The next step then is to evaluate each task asking the following questions:

Which tasks brings the most value to the organization you are working for?
Which tasks are repetitive enough to be automated?
Which tasks can be delegated or passed on to other in your team?
Which tasks can you do best and which ones do you struggle the most?
Which tasks do you enjoy the most?

As you evaluate your job through these questions, you can better understand not just how good of a fit it is for your as an individual but also how automation may transform your work in the coming years. As machine learning becomes more prevalent, the repetitive parts of your job are most likely to disappear.

Tasks on the rise

The MIT-IBM Watson report analyzed job listings over a period of ten years and identified groups of tasks that were in higher demand than others. That is, as job change, certain tasks become more valuable either because they cannot be replaced by machine learning or because there is growing need for it.

According to the research, tasks in ascendance are:

Administrative
Design
Industry Knowledge
Personal care
Service

Note that the last two tend to be part of lower wage jobs. Personal care is an interesting one (i.e.: hair stylist, in-home nurses, etc.). Even with the growing trend in automations, we still cannot teach a robot to cut hair. That soft but precise touch from the human hand is very difficult to replicate, at least for now.

How much of your job consists of any of the tasks above?

Tasks at risk

On the flip side, some tasks are in decline. Some of this is particular to more mature economies like the US while others have a more general impact due to wide-spread adoption of technologies. The list of these tasks highlighted in the report are:

Media
Writing
Manufacturing
Production

The last two are no surprise as the trend of either offshoring or mechanizing these tasks has been underway for decades. The first two, however, are new. As technologies and platforms abound, these tasks either become more accessible to wider pool of workers which makes them less valuable in the workplace. Just think about what it took to broadcast a video in the past and what it takes to do it now. In the era of Youtube, garage productions abound sometimes with almost as much quality as studio productions.

If your job consists mostly of these tasks, beware.

Occupational Shifts

While looking at tasks is important, overall occupations are also being impacted. As AI adoption increases, these occupations either disappear or get incorporated into other occupations. Of those, it is worth noting that production and clerical jobs are in decline. Just as an anecdote, I noticed how my workplace is relying less and less on administrative assistants. The main result is that everybody now is doing scheduling what before used to be the domain of administrative jobs.

Occupations in ascendance are those in IT, Health care and Education/Training. The latter is interesting and indicative of a larger trend. As new applications emerge, there is a constant need for training and education. This benefits both traditional educational institutions but also entrepreneurial start ups. Just consider the rise of micro-degrees and coding schools emerging in cities all over this country.

Learning as a Skill

In short, learning is imperative. What that means is that every worker, regardless of occupation or wage level will be required to learn new tasks or skills. Long gone are the days where someone would learn all their professional knowledge in college and then use it for a lifetime career. Continual training is the order of the day for anyone hoping to stay competitive in the workplace.

I am not talking just about pursuing formal training paths through academic degrees or even training courses. I am talking about learning as a skill and discipline for you day-to-day job. Whether from successes or mistakes, we must always look for learning opportunities. Sometimes, the learning can come through research on an emerging topic. Other times, it can happen through observing others do something well. There are many avenues for learning new skills or information for those who are willing to look for it.

Do you have a training plan for your career? Maybe is time to consider one.

AI Impact on Work: Latest Research Spells Both Hope and Concern

In a recent blog I explored Mckinsey’s report on the AI impact for women in the workplace. As the hype around AI subsides, a clearer picture emerges. The “robots coming to replace humans” picture fades. Instead, the more realistic picture is one where AI automates distinct tasks, changing the nature of occupations rather than replacing them entirely. Failure to understand this important distinction will continue to fuel the misinformation on this topic.

A Novel Approach

In this blog, I want to highlight another source that paints this more nuanced picture. The MIT-IBM Watson released a paper last week entitled “The Future of Work: How New Technologies Are Transforming Tasks.” The paper was significant because of its innovative methodology. It is the first research to use NLP to extract and analyze information on tasks coming from 170 million online job postings from 2010-2017 in the US market. In doing so, it is able to detect changes not only in the volume but also in job descriptions themselves. This allows for a view on how aspects of the same job may change over time.

The research also sheds light on how these changes translate into dollars. By looking at compensation, the paper can analyze how job tasks are valued in the labor market and how this will impact workers for years to come. Hence, they can test whether changes are eroding or increasing income for workers.

With that said, this approach also carry some limitations. Because they look only at job postings, they have no visibility into jobs where the worker has stayed consistently for the period analyzed. It is also relying on proposed job descriptions which often time do not materialize in reality. A job posting represents a manager’s idea for the job at that time. Yet circumstances around the position can significantly change making the actual job look very different. With that said, some data is better than perfect data and this researches open new avenues of understanding into this complex phenomenon.

Good News: Change is Gradual

For the period analyzed, researches conclude that the shift in jobs has been gradual. Machine learning is not re-shaping jobs a neck-breaking speed as some may have believed. Instead, it is slowly replacing tasks within occupations over time. On average, the worker is asked to perform 3.7 less tasks in 2017 as compared to 2010. As the researchers dig further, they also found a correlation between suitability to machine learning and faster replacement. Tasks more suitable to machine learning do show a larger average of replacement, at around 4.3 tasks while those not suited for machine learning show 2.9 average replacement. In general, jobs are becoming leaner and machine learning is making the process go faster.

This is good news but not necessarily reassuring. As more industries adopt AI strategies the rate of task replacement should increase. There is little reason to believe what we saw in 2010-2017 will repeat itself in the next 10 years. What the data signal demonstrates is that the replacement of tasks has indeed started. What is not clear is how fast it will accelerate in the next years. The issue is not the change but the speed in which it happens. Fast change can be de-stabilizing for workers and it is something that requires monitoring.

Bad News: Job Inequality Increased

If the pace is gradual, its impact has been uneven. Mid-income jobs are the worst hit by task replacement. As machine learning automate tasks, top tier middle income jobs move to the top income bracket while jobs at the bottom of the middle income income move to the low income jobs. That is, occupations in the low tier of the middle become more accessible to workers with less education or technical training. At the top, machine learning replace simpler tasks and those jobs now require more specialized skills.

This movement is translating into changes in income. Middle jobs has seen an overall erosion in compensation while both high and low income jobs have experienced an increase in compensation. This polarizing trend is concerning and worthy of further study and action.

For now, the impact of AI in the job market is further exacerbating monetary value of different tasks. The aggregate effect is that jobs with more valued tasks will see increases while those with less value will either become more scarce or pay less. Business and government leaders must heed to these warnings as they spell future trouble for businesses and political unrest for societies.

What about workers? How can these findings help workers navigate the emerging changes in the workplace? That is the topic for my next blog

AI and Women at the Workplace: A Sensible Guide for 2030

Even a few years in, the media craze over AI shows no sign of subsiding. The topic continues to fascinate, scare and befuddle the public. In this environment, the Mckinsey report on AI and Women at the workplace is a refreshing exception. Instead of relying on hyperboles, they project meaningful but realistic impact of AI on jobs. Instead of a robot apocalypse, they speak of a gradual shifting of tasks to AI-enabled applications. This is not to say that the impact will be negligible. Mckinsey still projects that between 40 – 160 M women may need to transition into new careers by 2030 worldwide. This is not a small number when the low end accounts for roughly population of California! Yet, still much less than other predictions.

Impact on Women

So why do a report based on one gender? Simply put, AI-driven automation will affect men and women differently in the workplace as they tend to cluster in different occupations. For example, women are overly represented in clerical and service-oriented occupations, all of which are bound to be greatly impacted by automation. Conversely, women are well-represented in health-care related occupations which are bound to grow in the period forecasted. These facts alone will assure that genders will experience AI impact differently.

There are however, other factors impacting women beyond occupation clusters. Social norms often make it harder for women to make transitions. They have less time to pursue training or search for employment because they spend much more time than men on house work and child care. They also have lower access to digital technology and participation in STEM fields than men. That is why initiatives that empower girls to pursue study in these areas are so important and needed in our time.

The main point of the report is not that automation will simply destroy jobs but that AI will move opportunity between occupations and geographies. The issue is less of an inevitable trend that will wipe out sources of livelihood but one that will require either geographic mobility or skill training. Those willing to make these changes are more likely to survive and thrive in this shifting workplace environment.

What Can You Do?

For women, it is important to keep your career prospects open. Are you currently working in an occupation that could face automation. How can you know? Well, think about the tasks you perform each day. Could they be easily learned and repeated by a machine? While all of our jobs have portions we wish were automated, if that applies to 60-80% of your job description, then you need to re-think your line of work. Look for careers that are bound to grow. That may not may simply learning to code but also consider professions that require human touch and cannot be easily replaced by machines. Also, an openness to moving geographically can greatly improve job prospects.

For parents of young girls, it is important to expose them to STEM subjects early on. A parent encouragement can go a long way in helping them consider those areas as future career options. That does not mean they will become computer programmers. However, early positive experiences with these subjects will give them the confidence later in life to pursue technical occupations if they so choose. A big challenge with STEM is the impression that it is hard, intimidating and exclusive to boys. The earlier we break these damaging paradigms the more we expand job opportunity for the women of the future.

Finally, for the men who are concerned about the future job prospects of their female loved ones, the best advice is get more involved in housework and child rearing. In short, if you care about the future of women in the workplace, change a diaper today and go wash those dishes. The more men participate in unpaid house work and child rearing the more women will be empowered to pursue more promising career paths.