Algorithms Archives

How is AI Hiring Impacting Minorities? Evidence Points to Bias

Thousands of resumes, few positions, and limited time. The story repeats itself in companies globally. Growing economies and open labor markets, now re-shaped by platforms like Linkedin and Indeed, a growing recruiting industry opened wide the labor market. While this has expanded opportunity, it left employers with the daunting task to sift through the barrage of applications, cover letters, resumes thrown in their way. Enters AI, with its promise to optimize and smooth out the pre-selection process. That sounds like a sensible solution, right? Yet, how is AI hiring impacting minorities?

Not so fast – a 2020 paper summarizing data from multiple studies found that using AI for both selection and recruiting has shown evidence of bias. As in the case of facial recognition, AI for employment is also showing disturbing signs of bias. This is a concerning trend that requires attention from employers, job applicants, citizens, and government entities.

Photo by Cytonn Photography on Unsplash

Using AI for Hiring

MIT podcast In Machines we Trust goes under the hood of AI hiring. What they found was surprising and concerning. Firstly, it is important to highlight how widespread algorithms are in every step of hiring decisions. One of the most common ways is through initial screening games that narrow the applicant pool for interviews. These games come in many forms that vary depending on vendor and job type. What they share in common is that, unlike traditional interview questions, they do not directly relate to skills relevant to the job at hand.

AI game creators claim that this indirect method is intentional. This way, the candidate is unaware of how the employer is testing them and therefore cannot “fake” a suitable answer. Instead, many of these tools are trying to see whether the candidate exhibits traits of past successful employees for that job. Therefore, employers claim they get a better measurement of the candidate fit for the job than they would otherwise.

How about job applicants? How do they fare when AI decides who gets hired? More specifically, how does AI hiring impact minorities’ prospects of getting a job? On the other side of the interview table, job applicants do not share in the vendor’s enthusiasm. Many report an uneasiness in not knowing how the tests’ criteria. This unease in itself can severely impact their interview performance creating additional unnecessary anxiety. More concerning is how these tests impact applicants with disabilities. Today, thanks to the legal protections, job applicants do not have to report disabilities in the interviewing process. Now, some of these tests may force them to do it earlier.

What about Bias?

Unfortunately, bias does not happen only for applicants with disabilities. Other minority groups are also feeling the pinch. The MIT podcast tells the story of an African-American woman, who though having the pre-requisite qualifications did not get a single call back after applying to hundreds of positions. She eventually found a job the old-fashioned way – getting an interview through a network acquaintance.

The problem of bias is not entirely surprising. If machine learning models are using past data of job functions that are already fairly homogenous, they will only reinforce and duplicate this reality. Without examining the initial data or applying intentional weights, the process will continue to perpetuate this problem. Hence, when AI is training on majority-dominated datasets, the algorithms will tend to look for majority traits at the expense of minorities.

This becomes a bigger problem when AI applications go beyond resume filtering and selection games. They are also part of interviewing process itself. AI hiring companies like Hirevue claim that their algorithm can predict the success of a candidate by their tone of voice in an interview. Other applications will summarize taped interviews to select the most promising candidates. While these tools clearly can help speed up the hiring process, bias tendencies can severely exclude minorities from the process.

The Growing Need for Regulation

AI in hiring is here to stay and they can be very useful. In fact, the majority of hiring managers state that AI tools are saving them time in the hiring process. Yet, the biggest concern is how they are bending power dynamics towards employers – both sides should benefit from its applications. AI tools are now tipping the balance toward employers by shortening the selection and interview time.

If AI for employment is to work for human flourishing, then it cannot simply be a time-saving tool for employers. It must also expand opportunity for under-represented groups while also meeting the constant need for a qualified labor force. Above all, it cannot claim to be a silver bullet for hiring but instead an informative tool that adds a data point for the hiring manager.

There is growing consensus that AI in hiring cannot go on unregulated. Innovation in this area is welcome but expecting vendors and employers to self-police against disparate impact is naive. Hence, we need intelligent regulation that ensures workers get a fair representation in the process. As algorithms become more pervasive in the interviewing process, we must monitor their activity for adverse impact.

Job selection is not a trivial activity but is foundational for social mobility. We cannot afford to get this wrong. Unlike psychometric evaluations used in the past that have scientific and empirical evidence, these new tools are mostly untested. When AI vendors claim they can predict job success by the tone of voice or facial expression, then the burden is on them to prove the fairness of their methods. Should AI decide who gets hired? Given the evidence so far, the answer is no.

AI for Good in the Majority World: Data Science Nigeria

Data Science Nigeria has an ambitious goal: to train 1 million Nigerian data scientists by the end of the decade. Yet, it does not end there, the non-profit aims to make the largest African nation a leading player in the growing global AI industry. Hence, DSN is a shining example of the growing trend of AI for good in the majority world.

AI holds great potential to solve intractable socioeconomic problems. It is not a silver-bullet solution, but a great enabler to speed up, optimize, and greatly improve decision making. Hence, it is not surprising to see the burgeoning AI for good trend emerging in the majority of the world. Yet, what makes DSN stand apart is that it goes a step further. It seeks not only to solve social problems but also to create economic opportunity that would not exist otherwise.

It is this abundance mentality that will best align AI with the flourishing of life.

Re-framing Who Are AI’s Customers

I learned about DSN while attending Pew Research recent webinar on AI ethics. One of its panelist was Dr. Uyi Stewart, DSN board member and IBM distinguished engineer, whose perspective stood out. While others discussed AI ethics in abstract terms, he proposed that AI should be about solving problems for 75% of the world population. That is, AI is not limited to solving complex business problems for the world’s largest corporations. Instead, it can and should be part of the daily life of those living in remote villages and cramped urban centers in the Southern Hemisphere.

Photo by Nqobile Vundla on Unsplash

How so? He went further to provide an example. The world’s poor today face life-and-death choice around the scarcity of resources. The farmers must contend with the fluctuations of a warming climate. The urban dweller, must make key decisions with very limited financial resources. Most of them already own a phone. Hence, he believes industry should develop decision support solutions through their devices so they can make better choices. These are not ways to optimize profit but can represent the difference between life and death for some.

Where most see a social problem, Dr. Stewart envisions a potent market opportunity.

From Scarcity to Abundance

Our economic system is mostly based on the concept of scarcity. That is, the idea that resources are finite and therefore must be allocated efficiently. It is scarcity mentality that drives the market to increase prices for commodities even when they are abundant. Moreover, companies and government may limit production of a product simply to simulate this effect and therefore achieve higher profit margins.

The digital economy has turned the concept of scarcity on its head. When knowledge is digitized and storage is cheap, we move from finite resources to limitless solutions. Even so, one must first optimize these solutions which is why AI becomes crucial in the digital economy. The promise of AI for good in the majority world is unleashing this wealth of opportunity in places where physical resources are scarce. DSN is leading the way by empowering young Nigerians to become data scientists. With this knowledge, they can unlock hidden opportunities in the communities they live.

By investing in the Nigerian youth, this organization is tapping into the majority world’s greatest resource. This is what AI for good is all about: technology for the flourishing of humanity in places of scarcity.

How AI and Faith Communities Can Empower Climate Resilience in Cities

AI technologies continue to empower humanity for good. In a previous blog, we explored how AI was empowering government agencies to fight deforestation in the Amazon. In this blog, we discuss the role AI is playing to build climate resilience in cities. We will also look at how faith communities can use AI-enabled microgrids to serve communities hit by climate disassters.

A Changing Climate Puts Cities in Harm way.

I recently listened to an insightful Technopolis podcast on how cities are preparing for an increased incidence of natural disasters. The episode discussed manifold ways city leaders are using technology to prepare, predict and mitigate the impact of climate events. This is a complex challenge that requires a combination of good governance, technological tools, and planning to tackle.

Climate resilience is not just about decreasing carbon footprint, it is also about preparing for the increased incidence of extreme weather. Whether there are fires in California, Tifoons in East Asia, or severe droughts in Northern Africa, the planet is in for a bumpy ride in the coming decades. They will also exacerbate existing problems such as air pollution, water scarcity and heat diseases in urban areas. Governments and civic society groups need to start bracing for this reality by taking bold preventive steps in the present.

Cities illustrate the costs of delaying action on climate change by enshrining resource-intensive infrastructure and behaviors. The choices cities make today will determine their ability to handle climate change and reap the benefits of resource-efficient growth. Currently, 51% of the world’s population lives in cities and within a generation, an estimated two-thirds of the world’s population will live in cities. Hence, addressing cities’ vulnerabilities will be crucial for human life on the planet.

Photo by Karim MANJRA on Unsplash

AI and Climate Resilience

AI is a powerful tool to build climate resilience. We can use it to understand our current reality better, predict future weather events, create new products and services, and minimize human impact. By doing so, we can not only save and improve lives but also create a healthier world while also making the economy more efficient.

Deep learning, for example, enables better predictions and estimates of climate change than ever before. This information can be used to identify major vulnerabilities and risk zones. For example, in the case of fires, better prediction can not only identify risk areas but also help understand how it will spread in those areas. As you can imagine, predicting the trajectory of a fire is a complex task that involves a plethora of variables related to wind, vegetation, humidity, and other factors

The Gifts of Satellite Imagery

Another crucial area in that AI is becoming essential is satellite imagery. Research led by Google, the Mila Institute and the German Aerospace Center harness AI to develop and make sense of extensive datasets on Earth. This in turn empowers us to better understand climate change from a global perspective and to act accordingly.

Combining integrated global imagery with sophisticated modeling capabilities gives communities at risk precious advance warning to prepare. Governments can work with citizens living in these areas to strengthen their ability to mitigate extreme climate impacts. This will become particularly salient in coastal communities that should see their shores recede in the coming decades.

This is just one example of how AI can play a prominent role in climate resilience. A recent paper titled “Tackling Climate Change with Machine Learning,” revealed 13 areas where ML can be developed. They include but are not limited to energy consumption, CO2 removal, education, solar energy, engineering, and finance. Opportunities in these areas include the creation of new low-carbon materials, better monitoring of deforestation, and cleaner transport.

Microgrids and Faith Communities

If climate change is the defining test of our generation, then technology alone will not be enough. As much as AI can help find solutions, the threat calls for collective action at unprecedented levels. This is both a challenge and an opportunity for faith communities seeking to re-imagine a future where their relevance surpasses the confines of their pews.

Thankfully, faith communities already play a crucial role in disaster relief. Their buildings often double as shelter and service centers when calamity strikes. Yet, if climate-related events will become more frequent, these institutions must expand their range of services offered to affected populations.

An example of that is in the creation of AI-managed microgrids. They are small, easily controllable electricity systems consisting of one or more generating units connected to nearby users and operated locally. Microgrids contain all the elements of a complex energy system, but because they maintain a balance between production and consumption, they operate independently of the grid. These systems work well with renewable energy sources further decreasing our reliance on fossil fuels

When climate disaster strikes, one of the first things to go is electricity. What if houses of worship, equipped with microgrids, become the places to go for those out of power? When the grid fails, houses of worship could become the lifeline for a neighborhood helping impacted populations communicate with family, charge their phones, and find shelter from cold nights. Furthermore, they could sell their excess energy units in the market finding new sources of funding for their spiritual mission.

Microgrids in churches, synagogues, and mosques – that’s an idea the world can believe in. It is also a great step towards climate resilience.

How Does AI Compare with Human Intelligence? A Critical Look

In the previous post I argued that AI can be of tremendous help in our theological attempt to better understand what makes humans distinctive and in the image of God. But before jumping to theological conclusions, it is worth spending some time trying to understand what kind of intelligence machines are currently developing, and how much similarity is there between human and artificial intelligence.Image by Gordon Johnson from Pixabay

The short answer is, not much. The current game in AI seems to be the following: try to replicate human capabilities as well as possible, regardless of how you do it. As long as an AI program produces the desired output, it does not matter how humanlike its methods are. The end result is much more important than what goes on ‘on the inside,’ even more so in an industry driven by enormous financial stakes.

Good Old Fashioned AI

This approach was already at work in first wave of AI, also known as symbolic AI or GOFAI (good old-fashioned AI). Starting with the 1950s, the AI pioneers struggled to replicate our ability to do math and play chess, considered the epitome of human intelligence, without any real concern for how such results were achieved. They simply assumed that this must be how the human mind operates at the most fundamental level, through the logical manipulation of a finite number of symbols.

GOFAI ultimately managed to reach human-level in chess. In 1996, an IBM program defeated the human world-champion, Gary Kasparov, but it did it via brute force, by simply calculating millions of variations in advance. That is obviously not how humans play chess.

Although GOFAI worked well for ‘high’ cognitive tasks, it was completely incompetent in more ‘mundane’ tasks, such as vision or kinesthetic coordination. As roboticist Hans Moravec famously observed, it is paradoxically easier to replicate the higher functions of human cognition than to endow a machine with the perceptive and mobility skills of a one-year-old. What this means is that symbolic thinking is not how human intelligence really works.

The Advent of Machine Learning

What replaced symbolic AI since roughly the turn of the millennium is the approach known as machine learning (ML). One subset of ML that has proved wildly successful is deep learning, which uses layers of artificial neural networks. Loosely inspired by the brain’s anatomy, this algorithm aims to be a better approximation of human cognition. Unlike previous AI versions, it is not instructed on how to think. Instead, these programs are being fed huge sets of selected data, in order to develop their own rules for how the data should be interpreted.

For example, instead of teaching an ML algorithm that a cat is a furry mammal with four paws, pointed ears, and so forth, the program is trained on hundreds of thousands of pictures of cats and non-cats, by being ‘rewarded’ or ‘punished’ every time it makes a guess about what’s in the picture. After extensive training, some neural pathways become strengthened, while others are weakened or discarded. The end result is that the algorithm does learn to recognize cats. The flip side, however, is that its human programmers no longer necessarily understand how the conclusions are reached. It is a sort of mathematical magic.

ML algorithms of this kind are behind the impressive successes of contemporary AI. They can recognize objects and faces, spot cancer better than human pathologists, translate text instantly from one language to another, produce coherent prose, or simply converse with us as smart assistants. Does this mean that AI is finally starting to think like us? Not really.

When machines fail, they fail badly, and for different reasons than us.

Even when machines manage to achieve human or super-human level in certain cognitive tasks, they do it in a very different fashion. Humans don’t need millions of examples to learn something, they sometimes do very fine with at as little as one example. Humans can also usually provide explanations for their conclusions, whereas ML programs are often these ‘black boxes’ that are too complex to interrogate.

More importantly, the notion of common sense is completely lacking in AI algorithms. Even when their average performance is better than that of human experts, the few mistakes that they do make reveal a very disturbing lack of understanding from their part. Images that are intentionally perturbed so slightly that the adjustment is imperceptible to humans can still cause algorithms to misclassify them completely. It has been shown, for example, that sticking minuscule white stickers, almost imperceptible to the human eye, on a Stop sign on the road causes the AI algorithms used in self-driving vehicles to misclassify it as a Speed Limit 45 sign. When machines fail, they fail badly, and for different reasons than us.

Machine Learning vs Human Intelligence

Perhaps the most important difference between artificial and human intelligence is the former’s complete lack of any form of consciousness. In the words of philosophers Thomas Nagel and David Chalmers, “it feels like something” to be a human or a bat, although it is very difficult to pinpoint exactly what that feeling is and how it arises. However, we can intuitively say that very likely it doesn’t feel like anything to be a computer program or a robot, or at least not yet. So far, AI has made significant progress in problem-solving, but it has made zero progress in developing any form of consciousness or ‘inside-out-ness.’

Current AI is therefore very different from human intelligence. Although we might notice a growing functional overlap between the two, they differ strikingly in terms of structure, methodology, and some might even say ontology. Artificial and human intelligence might be capable of similar things, but that does not make them similar phenomena. Machines have in many respects already reached human level, but in a very non-humanlike fashion.

For Christian anthropology, such observations are particularly important, because they can inform how we think of the developments in AI and how we understand our own distinctiveness as intelligent beings, created in the image of God. In the next post, we look into the future, imagining what kind of creature an intelligent robot might be, and how humanlike we might expect human-level AI to become.

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.

Union Tech: How AI is Empowering Workers

Is technology empowering or hindering human flourishing?

This week, I found a promising illustration of empowerment. While driving back from South Carolina, I listened to an episode from Technopolis podcast which explores how technology is altering urban landscapes. Just like in a previous post, the podcast did not disappoint. In this episode, they talk to Palak Shah from the National Domestic Worker Alliance digital lab. The advocacy group seeks innovative ways to empower 2.5 million nannies, house cleaners, and care workers in the United States. Because of its highly distributed workforce (most domestic workers work for one or a few households making it difficult to organize in a way that auto workers could), they quickly saw that technology was the best way to reach and engage the workers they trying to reach.

The lab developed two main products: the Alia platform and a La Alianza chatbot. The platform aggregates small contributions from clients to offer benefits for the workers. One of the biggest challenges with domestic workers is that they have no safety net. Most only get paid when they work and do not have health insurance. By pooling workers and getting an additional contribution from clients with little overhead, the platform is able to give the workers some of these benefits. The chatbot offers news and resources to over 200K domestic worker subscribers.

When the pandemic hit, the lab team with some help from Google was able to fully pivot in order to address new emerging problems. The Alia platform became a cash-transfer tool to help workers that were not getting any income. Note that most of them did not receive unemployment or the stimulus checks coming from the government. Furthermore, the chatbot surveyed domestic workers to better understand the impact of the pandemic on their livelihoods so they could adequately respond to their needs.

The NDWA lab story illustrates well the power of harnessing technology for human flourishing.

As a technology worker myself, I wonder how my work is expanding or hindering human flourishing. Some of us may not be doing work that is directly aligned with a noble cause. Yet, there are many ways in which we can take small steps re-direct technology towards a more human future.

Last week, in a history-making move, a group of Google employees formed the first union in a major technology company. Before that, tech employees have played crucial roles as whistleblowers for abuses and excesses from their companies. Beyond that, numerous tech workers have contributed their valuable skills for non-profit efforts in what is often known as the “tech for good” movement. These efforts range from hackathons to long-term projects organized by foundations embedded within large multinational companies.

These are just a few examples of how technology workers are taking steps to keep large corporations accountable and contribute to their communities. There are many other ways in which one can work towards human flourishing.

How is your work contributing to human flourishing today?

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

Category: Algorithms