Predictive Analytics

Predictive analytics is the use of algorithms and machine learning techniques to forecast future events in real-time. Leveraging vast data sets, these algorithms are able to predict potential risks and costs, or even an individual’s future behaviour. Thanks to rapid advancements in technology and the emergence of big data, predictive analytics has seen growing use in various industries, from providing health care treatment recommendations, to assessing and determining candidates for hire, and even to help police anticipate potential crimes and criminals. As technology continues to improve, data analytics and artificial intelligence will only continue to grow their capabilities and expand their applications. However, with predictive analytics becoming increasingly prevalent in decision-making processes that have direct and potentially life-changing impacts on people’s lives, this raises serious ethical concerns regarding algorithmic bias, transparency, and data privacy.

Ethical Challenges

Bias and Discrimination

Ever since the rise of the computer gaming industry brought back the resurgence of neural networks, experts have argued that deep learning is the most effective way to train an artificial intelligence system. Designed to mimic the way a human brain thinks and makes decisions, a network of thousands or millions of individual processing nodes are connected together in a neural net, which enables an algorithm to train itself to perform a task given a prepared training data set. However, according to Barocas and Selbst from Cornell University and UCLA respectively, “an algorithm is only as good as the data it works with." Zarsky, a professor and vice dean at Haifa University, argues that algorithms trained on biased data sets will not only inherit pre-existing biases from the aforementioned data set but also generate novel patterns of unfair bias and discrimination and reinforce these patterns in their decision-making processes. Some surmise an algorithm may even interpret inequalities in historical data as sensible patterns, which in turn supports existing societal biases; furthermore, detecting and addressing unfair bias and discrimination in algorithms for predictive analytics is particularly difficult as these frequently occur because of unintended consequences from using the algorithm and not the purposeful actions of an ill-intentioned programmer.

Transparency

Some assert that transparency as an ethical issue is in opposition of other ethical interests such as privacy. Transparency in algorithms requires both accessibility and comprehensibility of information about the algorithm, as an algorithm requires accessibility to its information in order to be effective. Many modern sophisticated artificial intelligence systems are trained via deep learning comprised of extensive neural networks that can reach up to fifty layers. As each layer adds complexity, Sloan and Warner assert that the human comprehensibility of these networks is affected.

Predictive Privacy

The term “predictive privacy” refers to the ethical challenges facing both privacy and data protection that are posed by the ability of algorithms to predict sensitive information about an individual using a large data set of other individuals . In 2019, the Electronic Privacy Information Center (EPIC) raised this very ethical concern in their official complaint to the Federal Trade Commission against HireVue, a recruiting-technology company, in which they said that “the company’s use of unproven artificial-intelligence systems that scan people’s faces and voices constituted a wide-scale threat to American workers. ”. Mühlhoff’s definition of a violation of predictive privacy is that “if sensitive information about that person or group is predicted against their will or without their knowledge on the basis of data of many other individuals, provided that these predictions lead to decisions that affect anyone’s social, economic, psychological, physical, … well-being or freedom. ” Most importantly, predictive privacy can still be violated regardless of the prediction’s accuracy. Any information predicted against one’s will that leads to life-affecting decisions could be considered a violation of predictive privacy, but when systems for data collection and processing are designed such that subjects cannot provide meaningful or informed consent , then perhaps predictive privacy needs to be raised more seriously, especially when people’s lives are potentially at stake.