Ethics of Artificial Intelligence
The field of “artificial intelligence” is gaining increasingly noticeable relevance in the software industry. Frequently used in headlines and marketing, “AI” is widely regarded as an inaccurate buzzword, especially due to the many abstract and vague arguments derived from its prevalence in science fiction. The cultural definition is often confused with its technical meaning: “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (5). Meanwhile, the seemingly invasive approach of this technology to take over more parts of human life has brought many ethical concerns to light. Critics argue that the spread of artificial intelligence poses risks to human dignity, accountability, transparency, and algorithmic bias. As this technology spreads, it is important to ensure that its decisions can be justified when being applied to the large scales afforded by modern computing.
The main defining feature in what is recognized as “artificial intelligence” is how it makes decisions. Most traditional computer programs, regardless of scale, are only capable of doing exactly what their users tell them to do. Conditions must be strictly defined, and all output is traceable; any responsibility for the program lies with its creator, since all behavior is explicitly specified. AI programs, on the other hand, have the capability to learn and inference from vast quantities of data in order to determine the best action to take. A naive approach to creating an AI might involve the construction of a large decision tree that updates on each execution, correcting itself and effectively learning from its mistakes. Practical applications of this often require huge amounts of data collection and processing power. As a result, tracing the specific cause of an output is difficult if not impossible to achieve given the amount of information used in this process.
Background of Practical Use
In recent years, the dependence on AI in widely used systems has vastly increased as a result of new innovation and a realization of useful applications. It has seen a wide range of uses, including interactive video games, automotive systems, and social moderation. This presence has provided a noticeable boost to the “data economy”, creating an incentive for companies to collect more information about their customers to benefit their business (3). Sidewalk Labs, a Google-affiliated company focused on urban infrastructure, is a prime example of the influence that this data can have. In 2017, Sidewalk Labs entered negotiations with a Canadian government agency, Waterfront Toronto, to redevelop a part of the city’s waterfront area as a testbed for emerging technologies. The initiative claims to target issues such as the sustainability, accessibility, and prosperity of urban communities while improving the quality of life for all (4). A planning document created by the company prior to this arrangement involved the use of tax and financing authority, local policing powers, location tracking, and an absurd amount of individual data collection from inhabitants (1). This data would be used to project accountability and reward good behavior, involving the establishment of a social credit system and becoming integrated with the economy in the area. The idea would effectively call for a complete algorithmic governance with no visible human involvement; a dystopian sci-fi writer’s dream.
Sidewalk Labs is still operating today, although its plans have been significantly reformed following heavy criticism. Shoshana Zuboff, the author of The Age Of Surveillance Capitalism, denounced the initiative as a “for-profit China” that would “use digital infrastructure to modify and direct social and political behavior”. Other critics have cited privacy concerns and worries that the proposal grants too much governing power for a vaguely defined purpose. This is largely representative of the general discourse around artificial intelligence, plagued with issues of transparency and accountability that are largely ignored by its users. The most significant problems of AI are not technical or philosophical, but fundamental in how it is used in the real world. As the scale and influence of technology increases, so does the threat it presents. Companies grow, technology becomes more relevant - “the trend goes from programming computers to programming people” (3).
Issues and Discourse
A computer - even an AI - cannot understand purpose, and only does exactly what its implementation tells it to. If this implementation is vague or obscured, lacking accountability or understanding, it just opens the door to vulnerabilities and failures in society. Artificial intelligence is only a means to an end, and even when it is used within its definition, it is just one component of the systems that are built around it. Replacing jobs and undermining human dignity is not something that is specific to artificially intelligent systems, but the furthered demand and influence of technology as a whole. The implementers of these systems need to have an understanding of the problems caused by disregarding ethical context.
Examples of racial and sexist bias in AI are plentiful. The Apple Card, a credit card system launched in August 2019, has been the subject of a wide amount of discourse after many discovered seemingly sexist determinations of credit limits given to them (2). Facial recognition programs have failed to recognize people with darker skin tones, and word embedding technologies have shown bias towards European names (10). Meanwhile, surveillance companies in China are using AI to create “Uyghur analytics”, an example of racial profiling that specifically targets a minority group with the intent of persecution (8). However, these are not problems that can be prevented with a better algorithm or more experience, nor can they be solved by theorizing about a future utopia in which technology rules us all. These are social and political issues that affect how any technology is built and used today. It is the decisions about the design and use of this software that are made by its creators, either without enough consideration or with the wrong consideration, that have led to this injustice.
Many experts in the field believe that a lot of these problems are caused by a general lack of diversity in the workforce. Nareissa Smith, a journalist with experience in law and technology, shares these concerns: “it’s unsurprising that companies staffed primarily by white men would fail to recognize the ways that their software cause problems for women, people of color, and other groups” (10). Excluding certain groups from the development process of these technologies, whether intentional or not, has clear effects on the industry. The institutional bias that still governs much of society is not exclusive to software, and companies need to have a better representation of these groups.
Recently, US lawmakers have introduced the Algorithmic Accountability Act, a bill that would require companies to audit large-scale automated systems for potential bias. The current legislation only directs companies to find a solution, but it marks a notable start to enforcing that technology must remain accountable in today’s world (10). Regulating the influence of this software is important because of its ability to facilitate discrimination at a large scale, and a clear review of this technology should be mandatory to prevent carelessness and protect human rights in an industry where they are often ignored.
Many supporters of the bill also call for “explainable AI”, the idea that algorithms should always present justification for their decisions. This offers significant potential to increase the transparency of these systems, improving both understanding and trust. However, some worry that this concept could be misconstrued. Lizzie Kumar, a data scientist and graduate student at the University of Utah, explains that the justifications provided by these algorithms could be “far removed from what humans actually desire from an explanation” (6). Machine learning models can be both inscrutable and non-intuitive, making it difficult to determine if a decision was justified from a legal or ethical standpoint, and legislating to solve this issue might not necessarily improve that aspect (9).
With the many ways that technology is becoming more integrated in everyday life, an understanding of the ways that it can affect its users is crucial in establishing a clear standard for its use. In the large automated systems that are enabled by AI, the injustices of discrimination and bias are easier to hide and have the potential for a much larger effect on humanity. Allowing companies to create opaque and unaudited systems with no human oversight is a significant cause for concern, and their increasing influence presents a considerable risk. However, it is important to consider cases where the technology is not the main culprit, and acknowledge the external factors that influence its use. While AI has the potential to provide clear and unbiased results, the biased and political motivation behind it is what shapes how it interacts with the real world.
"Sidewalk Labs Document Reveals Company's Early Vision for Data Collection, Tax Powers, Criminal Justice", by Tom Cardoso and Josh O'Kane
"About the Apple Card", by Jamie Heinemeier Hansson
"Will Democracy Survive Big Data and Artificial Intelligence?", by Dirk Helbing, Bruno S. Frey, Gerd Gigerenzer, Ernst Hafen, Michael Hagner, Yvonne Hofstetter, Jeroen van den Hoven, Roberto V. Zicari, Andrej Zwitter
"Introduction to the IDEA District", by Sidewalk Toronto
"Siri, Siri, in My Hand: Who's the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence", by Andreas Kaplan and Michael Haenlein
"Please Read These If You're "Doing" "Explainable" "AI"", by Lizzie Kumar
"Sidewalk Labs Digital Update Brings New Details, but Many Questions Remain", by James McLeod
"Hikvision Markets Uyghur Ethnicity Analytics, Now Covers Up", by Charles Rollet
"The Intuitive Appeal of Explainable Machines", by Andrew D. Selbst and Solon Barocas
"The Intelligence Is Artificial. The Bias Isn't", by Nareissa Smith