AI & Ethics: With great power comes great responsibility

Charlotte Murray
Feb 19, 2019 · 12 min read
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“With great power comes great responsibility” — Uncle Ben, Spiderman

Ethical Framework

The comprehensive report called Ethically Aligned Design was created to inform, upskill and support organisations that create AI of the importance of ethics in technologies that empower humanity. Created by hundreds of diverse thought leaders across different disciplines, the document outlines some key ethical principles that AI decision-making should adhere to.

  1. Accuracy: The degree to which the output is representative of the truth.
  2. Fairness: The decision-making is impartial and made irrespective of sensitive data.

1. Intelligibility

Today’s algorithms decide a lot of things about us — who gets hired, who gets fired, who gets a mortgage and who is a dangerous criminal — which can make what might be a difficult decision, simple. Knowing how and why these decisions are made is less simple. Organisations have historically hidden behind ‘black box’ systems that prevent scrutiny of their methodology, blaming complexity and intellectual property protection for the secrecy. However, several high-profile cases have questioned the validity of the black box decisions, which ignited the demand for ‘intelligible’ AI systems — those that are technically transparent and explainable.

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XAI Concept

2. Accuracy

Intelligibility helps determine the accuracy of algorithms. AI and machine learning have revolutionised the speed, efficiency and price of crunching large volumes of data. The powers and capabilities of the technologies have done everything from outperforming doctors at identifying lung cancer types and heart disease, writing sci-fi films and even beating two of Jeopardy’s best contestants. But, AI has also got it drastically wrong. In 2015, Google Photos automatically created the ‘gorilla’ tag on images containing black people. In 2016, Uber trialled autonomous self-driving cars in San Francisco that ran through six red lights, one of which was on a busy pedestrian crossing. Various researchers trained an algorithm to identify early signs of future suicide risk using patients’ historical medical records, but the methodology was overrun with false positives. For suicide prevention, one would not necessarily think that false positives are detrimental, but in fields such as medicine and criminology, false positives can be life changing.

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Google Photos mishap
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Bayesian inference

3. Fairness

Accurate insights are critical to fair decision-making. There is much debate around what is truly ‘fair’ decision making, but put simply, it is the equal, unbiased treatment of all groups/variables in a decision without influence from characteristics such as gender, race or religion. True fairness starts with data that represents a wide spectrum of characteristics, symbolism, beliefs and ideas, which influences how algorithms are trained and consequently how machine learning models make decisions. Unfortunately, algorithms and the data used to train them are generated by people and people are unavoidably and inherently biased. Microsoft learnt the hard way when they released a bot on Twitter called Tay, who became a racist, sexist, anti-sematic tyrant asking for all feminists to “burn in hell”. The bot itself was unbiased, but the training data generated by Twitter users was not.

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MIT Media Lab researcher Joy Buolamwini demonstrating how AI facial recognition works significantly better for white faces than black faces.
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AI Fairness 360 demo

Final Thoughts

For AI and machine learning to have a sustainable, positive influence on humanity, they must be guided by the same ethical morals and principles that humans themselves abide by. The sheer power and capabilities of autonomous systems have gradually removed the need for humans to be involved at various stages of the decision-making process, but with great power comes great responsibility. The expanding contexts that AI and machine learning are being applied in means that they need to abide by sets of complex, value-laden principles. The ultimate goal of these technologies is to serve the human; not the other way around, so the communities that build them are responsible and accountable to continually install knowledge and compliance of evolving ethical principles and the contexts in which they apply.

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Charlotte Murray

Written by

Behavioural scientist in training. My goal is to improve workplace well-being, experience and engagement using technology, science and data

Data Driven Investor

from confusion to clarity not insanity

Charlotte Murray

Written by

Behavioural scientist in training. My goal is to improve workplace well-being, experience and engagement using technology, science and data

Data Driven Investor

from confusion to clarity not insanity

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