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Mindset Training for AI Engineers

As machines are becoming more human, are humans becoming more like machines?

We need to keep the humans human, and it starts with those who are designing artificial intelligence.

The number of people drawn to artificial intelligence (AI) engineering is seeing the same exponential growth as AI itself. Simultaneously, we are seeing an increase in the number of people who are looking at the world and seeing the need for change, for movement in the direction of a world that works for everyone. Whether or not you are interested in AI or in social change, I think we can all agree that AI systems have an effect on all of us, and this trend is something worth paying attention to.

Each new development by AI engineers has great potential to profoundly impact our society and the experiences of individuals within it. Engineers spend a great deal of time looking at lines and lines of code on a screen, getting deep into a project. Engineering intangible software has the potential to feel extremely isolating and at some point, people can start to think like the machine itself. This is the point at which the mindset of the people creating AI systems becomes vitally important, and where I see a need to take a step back from the computer screen and take a look at our humanity.

Mindset describes the various ways to approach life and the way that we experience the events in our lives. There is an isolation that comes with engineering, but isolation is a much bigger theme in our culture that affects us all. As we all suffer with the realities we are dealt, we are inundated by mainstream individualized ideals that often involve putting others down to get ahead, remaining straight faced and striving to fit the cookie cutter roles we think we choose for ourselves. Mindset can feel like an automatic response to the world around us, like something that is not fully within our control. The truth about mindset, however, is that with some effort and encouragement, it can be changed- and this can alter the future for the individual and for the world.

I believe that AI has the potential to be an actionable space for creating change in how we relate to each other, how we interact with our environment, and in ensuring a future where there is a habitable and equitable world for the coming generations. AI is already affecting these things. How it affects them can be influenced by the people who are driving it- if we can understand and utilize this power with intention and mindfulness.

When engineering AI systems and looking at data, the numbers on the screen typically represent living breathing humans that we don’t know and may never meet. We must face the reality that we are creating artificial intelligence systems that will affect people’s lives in ways that we don’t understand.

This is a monumental power, and a huge responsibility. Engineers, AI leaders, and data scientists are making decisions that affect a very large population. All of the various differences and needs of people are boiled down to cold hard data. There is a lot of fear around this, which we will address later in this article.

What is the solution to quell this fear?

In an effort to preserve and protect the humanity of the people creating the technology that is increasingly automating our world, I am leading workshops and trainings in Mastering Mindset for AI Engineers. This article digs into what these trainings entail, as well as the results I hope to see by doing this work.

(This photo is actually from my second workshop, at Google Launchpad during the Demystifying AI Event hosted by Accel.AI)

My First Mastering Mindset Workshop

The first time I got up in front of a room full of AI engineers, I felt like I was talking to people from an entirely different culture who spoke an entirely different language. In fact, most of them did understand multiple languages, ones they learned through tedious practice — the languages of coding.

My own language, based in a soft science from my years as an undergrad studying alternative medicine and then completing a master’s in anthropology and social change, felt like it provided a rough translation at best. They didn’t laugh at my jokes, but no one left the room, and everyone engaged with the exercises I gave them and with the research I presented. They listened. It is an interesting sensation when people start listening to you. Since that first workshop, I have learned and continue to learn about the culture of AI engineering.

Why would an anthropologist of social change want to hold mindset training workshops for AI engineers? Because I want to see real change happen in the world. And the fastest way to make change is through technology and especially artificial intelligence, which is moving at an exponential pace.

This rapid advancement is not without side effects. There is a lot that is broken in the world and these systemic issues are being repeatedly built into new AI systems, predominantly under the radar of the engineers creating it and testing it.

Bias, particularly from systemic racism, has already been built into algorithms we trust to make some pretty serious decisions. In a recent study on the effects of recidivism algorithms, ProPublica uncovered concerning trends in the population of people who are receiving longer prison sentences, showing that sentencing is heavily imbalanced based on race. Cathy O’Neil also addresses issues such as this in her recent book, Weapons of Math Destruction. She observed that, “Unless we specifically make sure that the models do not unfairly punish poor people or black people, we will end up with systems that do. And that is what we are seeing.”

This is something that we need to join forces on and find a way to do better.

What’s in the Workshops

Learning AI is not just about the technological aspects, but about the social implications, the emotional and personal impact, and the responsibility of wielding this power. What I can do is provide AI engineers with insight, research, and the tools that will help them to see the importance and the impact their work can and will have. Here is a small snapshot of the workshops I lead:

We start with Carol Dweck’s work on growth mindset: what it is, how to apply it to AI engineering, how to recognize where we are stuck, and push through as a team. We then turn to Barbara Oakley’s work on learning how to learn and strengthening the mind, then explore neuroplasticity and how the brain actually learns. We touch on imposter syndrome, prevalent in engineers, although we don’t like to talk about it. We look to Cathy O’Neil’s work on what she calls ‘weapons of math destruction’ to understand the serious implications of well meaning algorithms that create real harm to the underprivileged. We talk about values that affect AI. We set goals for our own careers, and we start to take steps to work towards them. And of course we get deep into mindfulness, and how it affects our work, our bodies, and our lives.

In my workshops I present research to support and encourage not only learning but compassionate humanity in the humans creating AI, as we understand how AI is affecting our shared world. What drives this all home is that we get up and do several group exercises and writing exercises to combine theory with practice, then talk about how this works and the dialectic of logic and emotion.

Through the workshops, I pose the question: Can we use this knowledge paired with practice to not only work for harm reduction, but towards something better?

(Another shot from my second Mindset workshop of engineers participating in a group exercise)

I Believe that People Care, and Through Care, Intention, and Hard Work, We Can Change the World.

Through these mindset trainings, I support people who care about our future and who want to work toward the equality of all people. I work to prove that we can put that care into action with the tools and skills available in AI engineering, using well researched methods of developing change and growth in mindset, while getting clear on what values and goals we hold by finding compassion around our differences.

We are in exciting times. There are many passionate and motivated people at the wheel, steering the way to the future- and AI is a powerful tool that will help to get us there.

There is nothing moving faster than the development of AI. If we can catch that train and take over the engine room, can we steer in the direction to equality and connection?

This is a call to action. How do we move from theory into practice? How do we create the change that we know needs to happen, now? We have the tools, the fire is built, all it needs is a little spark. I am offering that spark.

Check out my Mastering Mindset workshops. Contact me if you’d like to hold one for your group or company, and keep an eye on Accel.AI to see when workshops are happening- both for mindset training and technical AI training. Let’s combine forces, learn from each other, and work together towards a better world for everyone.

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Effects of Automation & Retraining for the 4th Industrial Revolution

This post is an adaptation of a talk I recently gave for the Global AI Mind Web Series hosted and organized by Wetogether.co and GirlsinTech Taiwan.

[embed]https://youtu.be/etgljmHfIhg[/embed]

Think about it for a moment….

Whether you realize it or not, you are living in a world that has been dramatically altered due to artificial intelligence and automation. Advances in technology have been improving the efficiency of everyday processes and recreating our workforce in incremental ways.

It’s easy to adapt to these small changes as they are introduced, since they provide convenience and eliminate tedious tasks but as small changes add up they quickly create a new version of reality.

When is the last time you had to filter spam from your email inbox? Why are your favorite color of shoes always at the top of your search query on amazon? How does facebook know to show you ads for that new pizza place you were just discussing with your spouse? These examples are just a few of the capabilities that AI has today.

In order to help you truly understand the future effects of Artificial Intelligence, I want to introduce you to the hypothetical Chen family. Mrs. Chen is a lawyer, and Mr. Chen is a Doctor. These are highly skilled professions that require many years of schooling, logical reasoning, and accuracy to perform well at and be successful. When the Chen children grow up, the jobs their parents do will look much different than what they look like today.

Why is that? What will change in the next 10 or 20 years for these and other occupations?

Automation Revolutionizing our Economy

The answer is the onset of exponential growth in automated technology due to advances in Artificial Intelligence. To better understand the rate of change taking place now, we have to understand how automation has affected our economies in the past.

We have had several industrial revolutions led by automation.

Starting in the late 1700s, mechanization, water power, steam power, and railroads drastically changed the capabilities of our workforce and methods of transportation for goods around the world. In the mid to late 1800s, the invention and scale of electricity led to mass production leveraging human assembly lines in manufacturing. In the mid to late 1900s, electronics and computerization led to automated manufacturing.

We are now seeing the beginning of the fourth wave of an industrial revolution due to digitization, big data, internet of things and cognificationof everything.

Robot vs Human without Neural Networks

In the past there have been clear advantages to integrating robotics and automation with our workforce.

Robot’s have a higher capacity for:

  • Strength

  • Accuracy

  • Speed

  • They do not tire

  • They can do repetitive tasks

  • They can take and report measurements

On the other hand Humans have always had an advantage in:

  • Intelligence

  • Flexibility

  • Adaptability

  • Ability to Estimate

  • Skill Improvement over time (the ability to learn)

  • Emotional Awareness

How Automation Benefits Us

Automation has incredible benefits, the most prevalent being productivity.

Between 1850–1910, productivity grew 0.3% due to the introduction of the steam engine. Jumping ahead to 1993–2007, early robotics led to a 0.4% increase in productivity and in the IT sector, automation increased productivity 0.6% between 1995–2005. It’s predicted that automation due to Artificial Intelligence and Machine Learning will improve productivity by 0.8–1.4% on a global scale between 2015–2065.

Automation has been around for decades, what is different this time?

Deep Learning in AI

The difference is the onset of advances in Deep Learning in AI.

Deep learning is part of a broader family of machine learning methods based on learning data representations (also known as unsupervised learning), as opposed to task-specific algorithms (commonly found in supervised learning techniques).

The idea of using neural networks to perform deep learning tasks has been around since the 1950’s, formerly described using the concept of a perceptron. The difference being that Deep learning involves stacking many perceptrons or neural networks together through hidden layers — which perform multiple calculations and feature extractions within a fraction of time.

With the onset of Deep learning techniques, machines can now perceive and understand the world on their own.

These advances began with specific perceptual tasks using computer vision, training a neural network to recognize images using large datasets to perform feature extraction.

Deep learning has become the most popular approach in developing artificial intelligence today.

Why does this matter?

As Sundar Pichai, the CEO of Google stated, “The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first.”

AI will continue to be integrated with every aspect of our lives and every industry.

There are many predicted future benefits of this integration. Along with increase in productivity & efficiency, we will see an increase in:

  • Accuracy

  • Increasing safety or lowering risk

  • Decrease cost in labor and production

  • Increased throughput

  • Increased quality

  • Increase customer satisfaction

  • Allowance for high value activities — enabling us more time to do the things in life we appreciate most.

Areas Most Likely to See Automation

There is no industry that does not have partial automation potential. It’s estimated that about half of all the activities people are paid to do in the world’s workforce could potentially be automated by adapting currently demonstrated technologies. That amounts to almost $15 trillion in wages.

The activities most susceptible to automation are physical ones in highly structured and predictable environments, as well as data collection and processing.

It isn’t just blue collar work that is at risk for being automated. At an Executive level, a quarter to third of a CEOs time could be automated.

Automation on a Global Scale

According to the McKinsey Global Institute, areas of the world that have the highest potential for automation include — China, India, and the US.

  • China: 395.3 million employees potentially automatable

  • India: 235.1 million employees potentially automatable

  • United States: 60.6 million employees potentially automatable

The industries with the highest propensity for automation in those countries include:

  • Accommodation and food services — Almost 70%

  • Manufacturing — Almost 65%

  • Transportation & Warehousing — 60%

  • Retail trade — 55%

  • Agriculture, forestry, fishing and hunting — 50%

Where machines could replace humans — and where they can’t (yet)

Future Work Automation

The onset of advanced technology in Artificial Intelligence allows for exponential breakthroughs that’ll catapult our society into the future.

Within only a few years, we now have self driving cars powered by LIDAR systems. Automated food purchasing, food preparation, and food service. Autonomous manufacturing plants and delivery systems. Even autonomous medical and surgical systems which can perform with higher accuracy than humans.

[embed]https://youtu.be/89JojY5Ou8g[/embed]

Risks of Automation

Cybersecurity

Just as organizations can use artificial intelligence to enhance their security posture, cybercriminals may begin to use it to build smarter malware.

In the future, we may have attacker/defender AI scenarios play out.

This new generation of malware will be situation-aware, meaning that it will understand the environment it is in and make calculated decisions about what to do next. In many ways, malware will begin to behave like a human attacker: performing reconnaissance, identifying targets, choosing methods of attack, and intelligently evading detection.

With networked systems such as self driving cars — this creates huge potential for terrorism and disaster without proper security measures put in place for defense.

Increased Inequality

Wage gaps between the upper and lower class have been shown to increase dramatically with advances in automation. They will continue to do so at the exponential rate of advancement we are facing, forcing the middle class that we think of today to be nonexistent in the future.

Due to automation and advances in Artificial Intelligence, 47% of Jobs Will Disappear in the next 25 Years, According to Oxford University.

Those affected most will be low-skilled, low-wage workers.

Education and job training are more crucial than ever. The less you make in hourly wages, the more likely your job will be replaced by automation.

Bias in Machine Learning

Another huge risk in machine learning and automation is bias being perpetuated by AI systems.

These two pioneering women in tech and Artificial Intelligence, Melinda Gates and Fei Fei Li, have recognized this problem.

The phenomena of creating bias systems with AI has already been proven. It isn’t the fault of the algorithms or the machines, but the lack of proper awareness and oversight by engineers and researchers who are trained to look for these indicators.

If you don’t believe this is possible, just pick up a copy of Cathy O’Neil’s book Weapon’s of Math Destruction where she clearly outlines mathematical models or algorithms that claim to quantify important traits: teacher quality, recidivism risk, employability, and creditworthiness but have harmful outcomes and often reinforce inequality, keeping the poor poor and the rich rich. She gives in depth examples on how corruption that has been seen in finance is now being perpetuated by Big Data.

“…big data increases inequality and threatens democracy” — Cathy O’Neil

She also describes in depth how these systems create vicious feedback loops, much like our society.

“A person who scores as ‘high risk’ is likely to be unemployed and to come from a neighborhood where many of his friends and family have had run-ins with the law. Thanks in part to the resulting high score on the evaluation, he gets a longer sentence, locking him away for more years in a prison where he’s surrounded by fellow criminals — which raises the likelihood that he’ll return to prison. He is finally released into the same poor neighborhood, this time with a criminal record, which makes it that much harder to find a job. If he commits another crime, the recidivism model can claim another success. But in fact the model itself contributes to a toxic cycle and helps to sustain it.” — Cathy O’Neil

Datasets are already bias from systemic racism and our history of oppression. We need more underrepresented groups in tech and AI who can bring their unique life experiences to the table.

An “Existential Threat”

Founder and CEO of Tesla and SpaceX, Elon Musk has even warned that AI is our most “existential threat” to human civilization — potentially more dangerous than nukes.

Musk has called for precautionary, proactive government intervention. He thinks by the time we are reactive in AI regulation, it’ll be too late.

What can’t be automated?

So what is the light at the end of the tunnel? How do we prepare ourselves for this new age of automation? We must ask ourselves — what can’t be automated? We have to identify the things that are at core to our humanity.

I am talking about the things that are unique to our experience in the world. Specifically — Creativity and Empathy.

For now, machines cannot compete with us when it comes to tackling novel situations, and this puts a fundamental limit on the human tasks that machines will automate. We must embrace the novel, the uniqueness, the unconditional human condition.

A great example of creative evolution due to advances in technology is the invention of photography. When photography was invented, art, especially painting, changed. Portrait paintings went out of style. Did this mean that artists stopped painting? No, advances in technology allowed time for a series of art movements from pointillism, cubism, surrealism, abstract art and so on to emerge. This is likely what will happen with AI, it’s not that people will stop producing work, it will just get more creative and expressive, since there will be autonomous processes completing monotonous tasks, such as painting portrait after portrait.

Future Skills

So what does this mean for the future of work? The future state of any single job lies in the answer to a single question: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations? On frequent, high-volume tasks, machines are getting smarter and smarter.

Other than in oversight and regulation of machines, humans will still be needed to perform in unique and creative situations. We’ll be needed to connect with one another, provide entertainment, creative output, and continue to design the future as the world evolves.

In this way — Technology Is Only Making Social Skills More Important.

Nearly all job growth will be in occupations that are relatively social skill-intensive. High-skilled, hard-to-automate jobs will increasingly demand social adeptness.

The Harvard Business Review has reported, since 1980, “Job and wage growth has been strongest in occupations requiring both high cognitive and high social skills.” While demand for jobs requiring routine skills has declined.

They have also seen higher earnings increase for “multiskilled” individuals in the labor force.

The Development of AI

A high demand for engineers who can develop autonomous systems, coupled with massive job losses due to automation — means we have a great opportunity to retrain and upskill our workforce. According to a study from Paysa, in 2017, U.S. companies are planning to allocate more than $650 million to fuel the AI talent race.

Demand is so high for AI, large tech companies are competing to poach talent from Universities all over the country. Many in academia are making the switch to the corporate sector. Startups who need to incorporate AI into their platform just to be competitive in the market are vying for anyone who understands what AI even means today.

Design Principles in Industry 4.0

Along with the technical skills in AI engineering, individuals will be needed to create and implement principles around design and user experience. There are 4 design principles in what some call — Industry 4.0 as described by wikipedia:

Interoperability: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP)

Information transparency: The ability of information systems to create a virtual copy of the physical world by enriching digital plant models with sensor data. This requires the aggregation of raw sensor data to higher-value context information.

Technical assistance: First, the ability of assistance systems to support humans by aggregating and visualizing information comprehensibly for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber physical systems to physically support humans by conducting a range of tasks that are unpleasant, too exhausting, or unsafe for their human co-workers.

Decentralized decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of exceptions, interferences, or conflicting goals, are tasks delegated to a higher level.

Cooperation between humans and technology

So, how can we upskill to create a world where we can live and work alongside machines? Can we stop viewing automation as the enemy and learn to embrace its benefits while being adequately prepared for the risks?

Sustainable AI Development

Can we embrace new job opportunities to engineer advanced robots and systems?

Can we acknowledge that sustainable AI development means ethics, design, user experience, and long term effects on our society have to be part of the conversation?

In this critical moment of preparing machines to take on many of the responsibilities we have formerly entrusted to other human beings, we must look at ourselves, at our companies, at our societies, and ask;

Who are we? Who represents us? And who do we represent?

We must have more than a single voice, a uniform experience, a typical approach. We must work harder to have technology represent every one among us.

Remember the Chen Family?

In the near future nearly all legal research will be performed by algorithms and Ms. Chen would only be expected to argue trial cases. In this world it will be her ability to communicate the facts as researched by AI with the jury that will be the most important part of the job. As for Mr. Chen, much of the prescribing work and diagnosis will be completed by AI, further more Dr. Chen will be accompanied by nurse and physical therapy bots that will take over much of the medical manual labor performed by humans today.

Should their children choose to pursue the traditional careers of their parents, their work will be greatly integrated with machine learning systems and they will need not only the skills of their profession but an ability to be extraordinarily human in a machine heavy environment.

If instead they choose to pursue careers in AI and computer science their son will likely go on to learn the skills needed to be successful alongside advances in this technology, but unless things drastically change, their daughter will have a much harder time — as women only make up 18 percent of CS majors today.

Priming our workforce for the 4th Industrial Revolution

At Accel.AI, we are excited to launch a new set of workshops focusing on Human Development for AI Engineering. Starting with a workshop on Engineering Mindset for AI, led by our personal development mentor Jen Shae Roberts.

This 90 minute workshop will be diving into some of the well-researched tools to gain insight into understanding mindset, what our own mindset is, what it means, how it plays out in work and life, and what we can do about it. There will be four sections, which will later be developed into full day workshops: growth mindset, learning how to learn, values and goals, and mindfulness. We will be applying theory and practice with a combination of teaching and interactive exercises, giving the participants something solid to come away with as they start on their journey in becoming AI engineers.

Register for the workshop!

Thank you!

You can stay up to date on our progress, workshops, and plans going forward through our website, mailing list, meetup group, twitter and facebook page.

Join us in shaping the next generation of AI engineers and enthusiasts around the world!

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Working Together to Create Social Change through Deep Learning and AI

Tl:dr{ We are striving to create deep fundamental change, in the way that we learn and perceive ourselves and the world around us, in order to keep up with exponential technologies, and in order to relate to our pasts in new ways that serve to help us grow instead of damage us, so that we can work together to create a better world that works for everyone. To get plugged in, check out Accel.AI}

Lack of Diversity / Oppression in Tech

Currently we live in a very sick world, with twisted systems that don’t work for everyone, but for only a few; creating a hierarchy of worth of people and a categorization and simplification of everything. We are seeing these problems perpetuated through models and bias systems utilizing Artificial Intelligence and Machine Learning techniques, even if the intentions are to solve these same issues.

Cathy O’Neil, data scientist and author of Weapons of Math Destruction (2016), is fighting on the frontlines of this issue, along with Joy Buolamwini, a researcher and ‘poet of code’ who explores the intersection of social impact technology and inclusion. Buolamwini, having personally had to put a white mask over her black face to be recognized by a widely used computer facial recognition program, points out that “. . . training sets don’t just materialize out of nowhere, we actually can create them. There’s an opportunity to create full spectrum training sets that reflect a richer portrait of humanity.” ¹ O’Neil adds that “. . . unless we specifically make sure that the models do not unfairly punish poor people or black people, we will end up with systems that do. And that is what we are seeing.” ²

Contradictions in Tech / Diversity Numbers / AI Benefits

I have been hearing people calling each other hypocrites and calling out contradictions, which I think is good to notice, however, we also must take note that in these times of great change, where we are trying to live in ways that are quite different than how we were raised, we are all walking contradictions. We are all hypocrites. We must be kind to ourselves and others. We must work together across our differences and complexities.

The use of algorithmic systems to improve efficiency in job placement, prison sentencing, loans and healthcare would be great if they were truly fair. Unfortunately, they are proving not to be, and are often created by private companies who don’t always disclose their process, creating an impenetrable “black box” which may have unfair implications for all of humankind.

For example, ProPublica did an analysis of a for-profit company, Northpointe, which created algorithms to determine which criminals are more likely to commit future crimes. Guess what? It is inherently biased that black people are more likely to commit crimes than they actually are, and transversely, it shows that white people are less likely to commit crimes than in reality. Judges around the country have been ill-advisedly using this technology in sentencing, among other things. You can see how problematic this is, and how it perpetuates instead of remedies the disproportionate number of black folks in US prisons. In ProPublica’s article on this, they said that “. . . when a full range of crimes were taken into account — including misdemeanors such as driving with an expired license — the algorithm was somewhat more accurate than a coin flip.” ³ I don’t like those odds.

I am not trying to point fingers and call out the ‘real bad guys.’ As I said, we all make mistakes. As painful, embarrassing, and unpleasant as it is, we must admit to mistakes, big and small, and even share them with others, so that we can learn from our own mistakes as well as from the mistakes of our peers. These mistakes affect the lives of way too many people for them to be glazed over.

Growth Mindset as a Path to Learning / Developing Diversity

One super practical approach to learning and developing diversity is found in Carol Dweck’s work on growth mindset. “The growth mindset is the belief that you can cultivate and improve upon your abilities through practice and effort. Someone with a fixed mindset believes these abilities are predetermined and largely unchangeable.” ⁴ Growth mindset is fundamental. It also must be understood that the greater world and culture, what we have learned in schools, from parents, and in various social and work environments is all very focused around fixed mindset. This makes it a hard transition- even if you agree with growth mindset. Hence, we need to be using growth mindset to apply growth mindset.

It must be further acknowledged that there is a lot of very real oppression, discrimination, and threat of violence that many people face, both internally and externally, that make it a lot harder and more complicated a process than simply saying- “just change your mindset.” However, it is possible, and if we work together to effect the systems that stem from AI models and bias data sets , we can simultaneously teach the machines to learn as we learn, utilizing growth mindset.

Summary

This stuff is both incredibly simple and utterly complex. That is the beauty of life. When we are approaching AI, deep learning, and ever advancing automation, we cannot lose sight of this beauty and complexity. We also cannot forget the past that we are coming from; however I believe that in using these tools, we do not have to be damned by it. There are serious threats of the oppressive, dominating and discriminating nature of the systems that have been affecting us for far too long creeping their way into AI and perpetuating these abusive and despairing realities. However there is also an ability for us, as learners, to learn how to learn, and use that understanding to program AI systems into new ways of seeing the world that celebrates diversity, empowers those who have been oppressed, and creates a more egalitarian existence. To again quote Joy Buolamwini: “We now have the opportunity to unlock greater equality If we make social change a priority and not an afterthought.” ¹ We can do this by building platforms that identify bias, working with diverse teams to catch each other’s blind spots, and “. . .start thinking about how we create more inclusive code and employ inclusive coding practices.” ¹

Basically, let’s all work together to create a world that works for everyone. One of the places that we are striving for this is Accel.AI, which is a career accelerator program focused on inclusion, teaching the technical skills to enter the AI workforce as well as creating a holistic environment that can support learners no matter your background, emphasizing the power of diversity and the importance of not just what we do, but why.

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