Ethical concerns mount as AI takes bigger decision-making role Harvard Gazette

The task

of an article such as this is to analyse the issues and to deflate the

non-issues. Artificial intelligence (AI) and robotics are digital technologies

that will have significant impact on the development of humanity in

the near future. They have raised fundamental questions about what we

should do with these systems, what the systems themselves should do,

what risks they involve, and how we can control these. is ai ethical As instances of unfair outcomes have come to light, new guidelines have emerged, primarily from the research and data science communities, to address concerns around the ethics of AI. Leading companies in the field of AI have also taken a vested interest in shaping these guidelines, as they themselves have started to experience some of the consequences for failing to uphold ethical standards within their products.

This can erode trust in AI technologies and land a business in legal trouble if it violates its own policies or local laws. New York City passed a law requiring companies to audit their AI systems for harmful bias before using these systems to make hiring decisions. Members of Congress have introduced bills that would require businesses to conduct algorithmic impact assessments before using AI for lending, employment, insurance and other such consequential decisions.

Can AI be used ethically?

Beyond the initial conflict, the complexity of the relationship between the machines and their creators is another ongoing theme throughout the story. According to a 2019 report from the Center for the Governance of AI at the University of Oxford, 82% of Americans believe that robots and AI should be carefully managed. While reflections around the ethical implications of machines and automation deployment were already put forth in the ’50s and ’60s (Samuel, 1959; Wiener, 1988), the increasing use of AI in many fields raises new important questions about its suitability (Yu et al., 2018). This stems from the complexity of the aspects undertaken and the plurality of views, stakes, and values at play.

In employment, AI software culls and processes resumes and analyzes job interviewees’ voice and facial expressions in hiring and driving the growth of what’s known as “hybrid” jobs. Rather than replacing employees, AI takes on important technical tasks of their work, like routing for package delivery trucks, which potentially frees workers to focus on other responsibilities, making them more productive and therefore more valuable to employers. But its game-changing promise to do things like improve efficiency, bring down costs, and accelerate research and development has been tempered of late with worries that these complex, opaque systems may do more societal harm than economic good.

What Constitutes a Critical Theory?

In interface design on web pages or in games, this

manipulation uses what is called “dark patterns” (Mathur

et al. 2019). At this moment, gambling and the sale of addictive

substances are highly regulated, but online manipulation and addiction

are not—even though manipulation of online behaviour is becoming

a core business model of the Internet. Examples of gender bias in artificial intelligence, originating from stereotypical representations deeply rooted in our societies.

Furthermore, I also discuss how other topics in AI ethics, such as machine ethics or singularity, relate to the concept of power (Sect. 4.8). In the last section (Sect. 4.9), I briefly look at non-Western approaches to AI ethics and argue that the concern for emancipation and empowerment is present (although perhaps less dominant) in these approaches as well. While dispositional and episodic power focus on a single agent and specific instances of power, systemic and constitutive power are more structure-centric (Allen, 2016; Sattarov, 2019).

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

Mittelstadt (2019) critically analysed the current debate and actions in the field of AI ethics and noted that the dimensions addressed in AI ethics are converging towards those of medical ethics. However, this process appears problematic due to four main differences between medicine and the medical professionals on one side, and AI and its developers on the other. Firstly, the medical professional rests on common aims and fiduciary duties, which AI developers lack. Secondly, a formal profession with a set of clearly defined and governed good-behaviour practices exists in medicine. This is not the case for AI, which also lacks a full understanding of the consequences of the actions enacted by algorithms (Wallach and Allen, 2008). Thirdly, AI faces the difficulty of translating overarching principle into practices.

• While dispositional and episodic power focus on a single agent and specific instances of power, systemic and constitutive power are more structure-centric (Allen, 2016; Sattarov, 2019).
• It has a goal, and it achieves that goal without considering the effect of its plan on the goals of other agents; therefore, ethical planning is a much more complicated form of planning because it has to take into account the goals and plans of other agents.
• Under both definitions, privacy is understood as a dispositional power, more precisely, as the capacity to control what happens to one’s information and to determine who has access to one’s information or other aspects of the self.
• Furthermore, issues of garbage-in-garbage-out (Saltelli and Funtowicz, 2014) may be prone to emerge in contexts when external control is entirely removed.

Future AI ethics faces the challenge of achieving this balancing act between the two approaches. Given the relative lack of tangible impact of the normative objectives set out in the guidelines, the question arises as to how the guidelines could be improved to make them more effective. At first glance, the most obvious potential for improvement of the guidelines is probably to supplement them with more detailed technical explanations—if such explanations can be found. Ultimately, it is a major problem to deduce concrete technological implementations from the very abstract ethical values and principles.

How Ethics, Regulations And Guidelines Can Shape Responsible AI

Swartout said generative AI could be used to help a student brainstorm a topic before they begin writing. Posing questions like “Are there alternative points of view on this topic?” or “What would be a counterargument to what I’m proposing?” to generative AI can also be used to critique an essay, pointing out ways it could be improved, he added. Fears about using these tools to cheat could be alleviated with a process-based approach to evaluate a student’s work. “Students will need to judge when, how and for what purpose they will use generative AI. Their ethical perspectives will drive those decisions.” Teams from across Google could submit projects for review by RESIN, which provided feedback and sometimes blocked ideas seen as breaching the AI principles.

Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. While this topic garners a lot of public attention, many researchers are not concerned with the idea of AI surpassing human intelligence in the near or immediate future. It’s unrealistic to think that a driverless car would never get into a car accident, but who is responsible and liable under those circumstances?

In the following, two of these approaches—deontology and virtue ethics—will be selected to illustrate different approaches in AI ethics. The virtue ethics approach, on the other hand, is based on character dispositions, moral intuitions or virtues—especially “technomoral virtues” (Vallor 2016). In the light of these two approaches, the traditional type of AI ethics can be assigned to the deontological concept (Mittelstadt 2019). Ethics guidelines postulate a fixed set of universal principles and maxims which technology developers should adhere to (Ananny 2016). The virtue ethics approach, on the other hand, focuses more on “deeper-lying” structures and situation-specific deliberations, on addressing personality traits and behavioral dispositions on the part of technology developers (Leonelli 2016).