The growth of AI applications is not going to slow down, however, we
need to ensure that it is developed and used responsibly. With new
collaboration between disciplines such as law, psychology, economics,
business, engineering, technology and the social sciences, data sets are
being developed which more accurately reflect the demographics of the
society in which they exist and open source fixes are being created to
remedy potentially biased outcomes. By increasing diversity in the tech
industry, we will have more eyes and heterogenic perspectives overseeing
the development of AI. To be clear the answer is not to replace all human
decision-making with machines, but rather take advantage of the ability of
a machine to make decisions without noise (because an algorithm will
provide the same outcome for any given input, unlike the variability in
outcomes of human decision-making) and with less bias than humans
(because algorithms can be designed to review only the relevant
employment criteria unlike with human decisions).
Do the risks of incorporating AI into employment decisions outweigh
the benefits? From a purely legal standard point, it seems that despite claims
of increased discrimination using AI, scholars believe the risk of liability is
very small; however, the truth is more nuanced. Any potential
discrimination detected in outcomes will most likely stem from human
biases contained in data and not by virtue of the use of AI itself. AI alone will
not fix the diversity problem in tech but addressing the unconscious biases
prevalent in this industry is a first and vital step. It does not work to shame
management or require diversity training. Nor does it serve to delay
incorporating AI into your decision-making because of fear of
discriminatory results. What works is removing subjective criteria from
employment decisions, improving working conditions and the culture for
everyone in these tech companies, and providing oversight and
accountability for creating a diverse working environment. Most
importantly, while it is clear that bias cannot be removed from human
decision-making, it can be mitigated with machine decision-making. In fact,
with the rapid development of responsible AI, there may come a time in the
not so distant future when courts will find companies still using human
decision-making in employment to be a prima facie showing of
discrimination.
While this paper mentions specific solutions as examples of ways to
incorporate AI into employment decision-making, it is not meant as a
limitation, but rather a starting place. It is intended to serve as an alternative