AI Ethics and Considerations

Shortly after I began my machine learning courses, it dawned on me that there is an absurd exaggeration in the media concerning the state of Artificial Intelligence. Many are under the impression that artificial intelligence is the study of developing conscious robotic entities soon to take over planet earth. I typically brace myself whenever someone questions what I study since my response is often prematurely met with a horrified gasp or angry confrontation. And understandably so.
Conscious Robotic Entities Soon to Take Over?
However, the reality is that Machine Learning is not a dangerous magic genie, nor is it any form of a conscious entity. For simplicity’s sake, I typically say that the essence of AI is math. Some say it’s just ‘glorified statistics’. Or as Kyle Gallatin has so eloquently put it, ‘machine learning is just y=mx+b on crack.‘
Of course, this is a simplification since machine learning pulls from many disciplines such as computer science, neuroscience, mathematics, the scientific method, etc. But the point is that the media is suffused with verbiage that makes it feel as though we are in direct danger of being taken over by artificially intelligent beings.
The truth is, we are not. But there are many other insidious issues in the production of machine learning that often goes overlooked. Rachel Thomas, a co-founder of fast.ai, has mentioned that she, along with other machine learning experts, believe that the ‘hype about consciousness in AI is overblown’ but ‘other (societal) harms are not getting enough attention’. Today, I want to elaborate on one of these societal harms that Rachel addresses: that ‘AI encodes and magnifies bias’.
The Real Hazard of Machine Learning: Garbage In Garbage Out
The most unsettling aspect of this – the idea of AI magnifying bias – is that the very promise of machine learning in the automation of social processes is to hold the highest degree of neutrality. It is well known that doctors can hold bias during diagnosis in healthcare or a jury may hold bias during sentencing in criminal justice. Machine learning should ideally synthesize a large amount of variables in the record and provide a neutral assessment.
"But what happened was that machine learning programs perpetuated our biases on a large scale. So instead of a judge being prejudiced against African Americans, it was a robot." – Brian Resnick
We expect the model to be objective and fair; it is this disillusioned position of objectivity that makes the entire ordeal feel insidious and particularly disappointing.
So how does this happen?

"Garbage in garbage out" is a well known computer science axiom that means poor quality input produces poor quality output. Typically, ‘non-garbage’ input would refer to clean, accurate, and well-labeled training input. However, we can now see that our garbage input could very well be a polished, accurate representation of our society as it has acted in the past. The real hazard in machine learning has less to do with robotic conscious entities and more to do with another type of conscious entity – human beings. When societally biased data is used to train a machine learning model, the insidious outcome is a discriminatory machine learning model that predicts the societal biases we aim to eliminate.
Higher Accuracy != Better Social Outcomes
The issue extends further from prediction and towards perpetuation; we create a type of reinforcement loop.
For example, let’s say that a business owner wants to predict which of their customers would be likely to buy certain products so they could offer a special bundle. They go on to ask a data scientist to build a predictive algorithm and use this to advertise to the select group. At this point, the model is not simply predicting which customers will purchase – it is reinforcing it.
While innocuous in this example, this can lead to harmful outcomes for social processes. This is exactly what led to these unanticipated headlines:

Again, if our application is directed towards medical care for the purpose of predicting which group should get more attention based on prior data, we are not simply predicting for the sake of optimization, we are now actively magnifying and perpetuating prior disparities.
So do we abolish machine learning because we knew it would lead to world destruction?
In short, no. But perhaps we should reimagine the way we practice machine learning. As previously mentioned, when I first began to practice machine learning, the over-exaggerated commonplace fear of artificial intelligence developing consciousness began to humor me a bit. I thought that perhaps the worst thing that could happen would be misuse like that of any tool we have, albeit misuse is perhaps more apparent of a physical tool than that of a digital tool.
However, the short film, ‘Slaughterbots’ by Alter on YouTube provoked a lot of thought regarding ethics and the possible dangers of autonomous artificial intelligence. The primary reason that the ‘Future of Life Institute’ created the film was to communicate the following idea: "Because autonomous weapons do not require individual human supervision, they are potentially scalable weapons of mass destruction – unlimited numbers could be launched by a small number of people."
In the context of this short film, the drones were exploited with the intent to harm. However, could disastrous unintentional repercussions arise from the use of A.I. systems? What would happen if we create AI to optimize for a loosely defined goal and loosely defined restraints without any supervisory precautions, and realize it was more than we bargained for? What if we create a system with great intentions to be used for social good but we wind up with catastrophic and irreversible damages? The lack of consciousness becomes irrelevant and yet doesn’t minimize the potential harm.
Then, I began stumbling across relevant resources that challenged the current standard model of artificial intelligence and addressed these issues which is what ultimately led to this synthesis of a blog post.
Inverse Reinforcement Learning
The first had been ‘Human Compatible’ by Stuart Russell which suggests that the standard model of AI is problematic due to the lack of intervention. In the current standard model, we focus on optimizing our initially set metrics without any human-in-the loop supervision. Russell challenges this with the hypothetical situation that we realize after some time that the consequences of our initial goals weren’t exactly what we wanted.
Instead, Stuart proposes that rather than using our AI systems to optimize for a fixed goal, that we create them with flexibility to adapt to our potentially vacillating goals. This means programming in a level of uncertainty in the algorithm where it cannot be completely certain that it knows our goals, so it will deliberately ask if it needs to be redirected or switched off. This is known as ‘Inverse Reinforcement Learning.’
Below you can see the difference between the common reinforcement learning goals and inverse reinforcement learning goals:

With traditional reinforcement learning, the goal is to find the best behavior or action to maximize reward in a given situation. For example, in the domain of self-driving cars, the model will receive a small reward for every moment it it remains centered on the road and receives a negative reward if it runs a red light. The model is moving through the environment trying to find the best course of action to take to maximize reward. Therefore, a reinforcement learning model is fed a reward function and attempts to find the optimal behavior.
However, sometimes the reward function is not obvious. To account for this, inverse reinforcement learning is fed a set of behaviors and it tries to find the optimal reward function. Given these behaviors, what does the human really want? The initial goal of IRL was to uncover the reward function under the assumption that the given behavior is the most favorable behavior. However, we know that this isn’t always the case. Following this logic, this process may help us unveil the ways in which humans are biased which would, in turn, allow us to correct future mistakes through awareness.
Biased COMPAS Algorithm
Another resource that is relevant and timely is the episode ‘Racism, the criminal justice system, and data science’ from Linear Digressions. In this episode, Katie and Ben tactfully discuss COMPAS, an algorithm that stands for Correctional Offender Management Profiling for Alternative Sanctions. This algorithm is legal to be utilized by judges during sentencing in a few US states to predict the likeliness of a defendant committing a crime again.

However, various studies have challenged the accuracy of the algorithm, discovering racially discriminatory results despite lacking race as an input. Linear Digressions explores potential reasons that racially biased results arise and wraps up with lingering string of powerful, thought-provoking ethical questions:
What is a fair input to have for an algorithm? Is it fair to have an algorithm that is more accurate if it introduces injustice when you consider the overall context? Where do the inputs come from? In what context will the output be deployed? When inserting algorithms into processes that are already complicated and challenging, are we spending enough time examining the context? What are we attempting to automate, and do we really want to automate it?
This last string of questions that Katie so neatly presents at the end of episode are wonderfully pressing questions that left a lasting impression given the fact that I am such a huge proponent of machine learning for social good. I am positive that these considerations will become an integral part of each complicated, data-driven, social problem I aim to solve using an algorithm.
Final Thoughts and Reflections
These models have hurt many people on a large scale while providing a false sense of security and neutrality, but perhaps what we can gain out of this is the acknowledgement of the undeniable underrepresentation in our data. The lack of data for certain minority groups are evident when the algorithms plainly do not work with these groups.
Bias is our responsibility to, at the very least, recognize, so that we can push initiatives forward to reduce them.
Moreover, in "What Do We Do About the Biases in AI," James Manyika, Jake Silberg and Brittany Presten present six ways in which management teams can maximize fairness with AI:
- Remain up to-date on the research surrounding Artificial Intelligence and Ethics.
- Establish a process that can reduce bias when AI is deployed
- Engage in fact-based conversations around potential human biases
- Explore ways in which humans and machines can integrate to combat bias
- Invest more efforts in bias research to advance the field
- Invest in diversifying the AI field through education and mentorship
Overall, I am very encouraged by the capability of machine learning to aid human decision-making. Now that we are aware of the bias in our data, we are responsible to take action to mitigate these biases so that our algorithm could truly provide a neutral assessment.
In light of these unfortunate events, I am hopeful that in the coming years, there will be more dialogue on AI regulation. There are already wonderful organizations such as AI Now, among others, that are dedicated to research that revolves around understanding the social implications of artificial intelligence. It is now our responsibility to continue this dialogue and move forward to a more transparent and just society.

Articles Used for Figure II:
- AI is sending people to jail – and getting it wrong
- Algorithms that run our lives are racist and sexist. Meet the women trying to fix them
- Google apologizes after its Vision AI produced racist results
- Healthcare Algorithms Are Biased, and the Results Can Be Deadly
- Self-Driving cars more likely to drive into black people
- Why it’s totally unsurprising that Amazon’s recruitment AI was biased against women