Definition of Key Terms
AI is a broad research field that does not fit neatly into a single precise definition. However, John McCarthy, one of the founders of AI research, defines “AI [as] the field of getting a computer to do things which when done by people, are said to involve intelligence.”1 Stated another way, AI is “the science of making machines smart.”2
AI has different sub-categories, one of which is machine learning. Machine learning provides “data-driven predictions”3 and refers broadly to the science of enabling computers to “learn” through the development of algorithms that “discover correlations or patterns in the data.”4 Over the past 10 years, machine learning has become increasingly popular with its corresponding reliance on big data,5 “the lifeblood of any AI application.” 6 Big data is “defined as information that is large in scale and complex in its interrelationships.”7
An algorithm is “a set of well-defined, step by step instructions for a machine to solve a specific problem and generate an output using a set of input data. AI algorithms involve complex mathematical codes that are designed to enable the machines to learn from new input data and develop new or adjusted output based on the learnings.”8 End-users rely on AI to either make decisions for them or to assist them in decision-making.9 Algorithms that impact our daily lives include Netflix’s sorting feature that suggests movies the subscriber may enjoy and Google’s sorting feature that determines the order of what a user sees in response to a search request.
AI’s Beneficial Uses
AI provides many positive benefits across different industries through its ability to provide meaningful predictions based on its quick, efficient, and cost-effective analyses of data sets. Its positive benefits are evident particularly in the health care field with AI’s ability to save lives. Recently, researchers designed an algorithm that accurately predicts if a patient will experience acute kidney injury within 48 hours of the medical occurrence. 10 Another algorithm accurately predicts which skin cancers respond best to certain immunotherapies.11
Likewise, financial services companies are relying increasingly on AI-based algorithms for, among other things, evaluating consumer credit risk, customer identification and fraud assessments, portfolio management, and consumer marketing.12 As with the health care field, proponents argue that AI technology is invaluable because it enables lenders to make “fairer, more responsible loan decisions”13 and promotes greater societal inclusion by providing lenders with the ability to rely on “alternative data” in order to make credit available to more underserved consumers.14
Similarly, human resources managers across industries are turning to AI to assist with employment-related tasks such as recruiting, hiring, compensation analysis, employee retention and promotion decisions. Echoing industry proponents’ arguments, human resources managers contend that AI is a critically important tool because it reduces risks that are associated with human errors in decision-making; expands the universe of potential applicants whom employers can interview; and evaluates extensive and complicated compensation and other employment-related data in a quicker, accurate, and more cost-effective manner.
This article was originally published in New Jersey Labor and Employment Law Quarterly's May 2021 issue. To read the full article, download the PDF.
- FINRA, Artificial Intelligence (AI) in the Securities Industry, FINRA Report, at 2 (June 10, 2020), available at finra.org.
- Frederick Zuiderveen Borgesius, Discrimination, Artificial Intelligence and Algorithmic Decisionmaking (2018), Directorate General of Democracy, Council of Europe at 8, available at rm.coe.int/.
- Id. at 9.
- Id.; FINRA, supra n.1, at 2.
- Borgesius, supra n.2, at 9.
- Nicholas Schmidt and Bryce Stephens, An Introduction to Artificial Intelligence and Solutions to the Problems of Algorithmic Discrimination, 73 The Quarterly Report, No. 2, 2019 at 134.
- FINRA, supra n.1, at 13.
- Id. at 4.
- Sharon Hoffman and Andy Podgurski, Artificial Intelligence and Discrimination in Health Care, Yale Journal of Health Policy, Law and Ethics 19:30, 2020 at 9, available at digitalcommons.law.yale.edu/.
- Starre Vartan, AI Can Predict Kidney Failure Days in Advance, Scientific American (July 31, 2019), available at scientificamerican.com/.
- Paul Johannet, Nicolas Coudray, Douglas M. Donnelly, et al., Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma, 27 Clinical Cancer Research, Issue 1 (January 2021), available at clincancerres.aacrjournals.org/.
- Arthur Bachinskiy, The Growing Impact of AI in Financial Services: Six Examples, Trends Data Science (Feb. 21, 2019), available at towardsdatascience.com/.
- BPI, Artificial Intelligence: Recommendations for Principled Modernization of the Regulatory Framework (Sept. 14, 2020) at 15, 22, available at bpi.com/.
- Britt Faircloth, The Risks and Management of Algorithmic Bias in Fair Lending, ABA Bank Compliance Magazine (Nov/Dec. 2019).
