Ethics of Artificial Intelligence

Company spokesperson Brian Gabriel says 10 percent of RESIN staffers will remain in place while 90% of the team were transferred to trust and safety, which fights abuse of Google services and also resides in the global affairs division. The rationale for the changes and how responsibilities will be broken up couldn’t be learned. Some of the sources say they have not been told how AI principles reviews will be handled going forward. RESIN’s role has looked uncertain since its leader and founder Jen Gennai, director of responsible innovation, suddenly left that role this month, say the sources, who spoke on the condition of anonymity to discuss personnel changes. Gennai’s LinkedIn profile lists her as an AI ethics and compliance adviser at Google as of this month, which sources say suggests she will soon leave based on how past departures from the company played out. The deal comes after UNESCO in 2021 adopted its Recommendation on the Ethics of Artificial Intelligence, which is based on “the promotion and protection of human rights, human dignity, and ensuring diversity and inclusiveness”.

As noted by a perceptive reviewer, ML systems that keep learning are dangerous and hard to understand because they can quickly change. Thus, could a ML system with real world consequences be “locked down” to increase transparency? If not, transparency today may not be helpful in understanding is ai ethical what the system does tomorrow. This issue could be tackled by hard-coding the set of rules on the behaviour of the algorithm, once these are agreed upon among the involved stakeholders. This would prevent the algorithm-learning process from conflicting with the standards agreed.

Can AI be used ethically?

Biased algorithms or in other ways biased AI systems can lead to discriminatory outcomes (e.g., continuously misidentify certain demographics as a threat or potential criminal) and therefore violate the principle of just and fair AI. Solidarity in AI would imply that the benefits of AI should be redistributed from those who are disproportionally benefitted by this new technology to those who turn out to be most vulnerable to it (e.g., those who are unemployed due to automation). AI projects built on biased or inaccurate data can have harmful consequences, particularly for underrepresented or marginalized groups and individuals.

Increasingly, nonprogrammers can set up off-the-shelf, pre-built AI tools as they prefer. We have to adhere to the strictest standards, so we’re seeing that Europe is really paving the way, and I think states are starting to follow,” Zuloaga said. Piers Turner’s research on data ethics was funded in part by a grant from Facebook and from the Risk Institute at the Fisher College of Business. However, the agency warns that the technology ‘is bringing unprecedented challenges’.

Stakeholders in AI ethics

An interesting proposal comes from Berk (2019), who asked for the intervention of super partes authorities to define standards of transparency, accuracy and fairness for algorithm developers in line with the role of the Food and Drug administration in the US and other regulation bodies. A shared regulation could help in tackling the potential competitive disadvantage a first mover may suffer. The development pace of new algorithms would be necessarily reduced so as to comply with the standards defined and the required clearance processes. In this setting, seeking algorithm transparency would not be harmful for their developers as scrutiny would be delegated to entrusted intermediate parties, to take place behind closed doors (de Laat, 2018).

It should not be the objective of ethics to stifle activity, but to do the exact opposite, i.e. broadening the scope of action, uncovering blind spots, promoting autonomy and freedom, and fostering self-responsibility. If

one takes machine ethics to concern moral agents, in some substantial

sense, then these agents can be called “artificial moral

agents”, having rights and responsibilities. However, the

discussion about artificial entities challenges a number of common

notions in ethics and it can be very useful to understand these in

abstraction from the human case (cf. Misselhorn 2020; Powers and

Ganascia forthcoming). It appears that lowering the hurdle to use such systems (autonomous

vehicles, “fire-and-forget” missiles, or drones loaded

with explosives) and reducing the probability of being held

accountable would increase the probability of their use. The crucial

asymmetry where one side can kill with impunity, and thus has few

reasons not to do so, already exists in conventional drone wars with

remote controlled weapons (e.g., US in Pakistan).

The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work

“Consider that you, in the more distant future, own a robot and you ask it to get you an umbrella because you see that it might rain today. 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. In this case, the robot might choose a plan to achieve your goal that, at the same time, harms some goal of Mr. Mean. Using AI tools that let users opt in to sharing personal data as opposed to making them opt out, automate workflows within smart greenhouses and power self-driving cars that prioritize safety over efficiency are all examples of ethical AI use.

• There is no universal, overarching legislation that regulates AI practices, but many countries and states are working to develop and implement them locally.
• Nevertheless, this contradicts the observation that AI has been making such massive progress for several years precisely because of the large amounts of (personal) data available.
• Countless news reports — from faulty and discriminatory facial recognition to privacy violations to black box algorithms with life-altering consequences — have put it on the agendas of boards, CEOs, and Chief Data and Analytics Officers.
• Researchers, politicians, consultants, managers and activists have to deal with this essential weakness of ethics.
• At the center of its attention is not human conduct, but the ways in which humans are affected by AI technology.

AI ethics—or ethics in general—lacks mechanisms to reinforce its own normative claims. Of course, the enforcement of ethical principles may involve reputational losses in the case of misconduct, or restrictions on memberships in certain professional bodies. Researchers, politicians, consultants, managers and activists have to deal with this essential weakness of ethics. However, it is also a reason why ethics is so appealing to many AI companies and institutions. When companies or research institutes formulate their own ethical guidelines, regularly incorporate ethical considerations into their public relations work, or adopt ethically motivated “self-commitments”, efforts to create a truly binding legal framework are continuously discouraged. Ethics guidelines of the AI industry serve to suggest to legislators that internal self-governance in science and industry is sufficient, and that no specific laws are necessary to mitigate possible technological risks and to eliminate scenarios of abuse (Calo 2017).

Exploitative Labor Practices

“The USC Center for Generative AI and Society’s new report is an invitation to educators, policymakers, technologists and learners to examine how generative AI can contribute to the future of education.” As AI technologies become more prevalent in the classroom, it is essential for educators to consider the ethical implications and foster critical thinking skills among students. Taking a thoughtful approach, educators will need to guide students in evaluating AI-generated content and encourage them to question the ethical considerations surrounding the use of AI. Seeking AI principles guidance is not mandatory for most teams, unlike reviews for privacy risks, which every project must undergo.

Like any critical theory, the purpose of AI ethics is not merely to analyze or diagnose society, but also to change it. Both critical theory and AI ethics have a practical goal, namely that of empowering individuals and protecting them against systems of power. But while critical theory is concerned with society at large, AI ethics focuses on the part that a particular type of technology plays in society. Hence, we could say that AI ethics is a critical theory, which focuses on the ways in which human emancipation and empowerment are or could be hindered by AI technology. The ethics of artificial intelligence (AI) is an upcoming field of research that deals with the ethical assessment of emerging AI applications and addresses the new kinds of moral questions that the advent of AI raises. The argument presented in this article is that, even though there exist different approaches and subfields within the ethics of AI, the field resembles a critical theory.

Artificial Intelligence: examples of ethical dilemmas

First, the business world and the workplace, rife with human decision-making, have always been riddled with “all sorts” of biases that prevent people from making deals or landing contracts and jobs. Health care experts see many possible uses for AI, including with billing and processing necessary paperwork. And medical professionals expect that the biggest, most immediate impact will be in analysis of data, imaging, and diagnosis. Imagine, they say, having the ability to bring all of the medical knowledge available on a disease to any given treatment decision.

This involves adopting stringent ethical guidelines and actively creating global standards and regulatory frameworks. The tech industry must prioritize legal certainty and a comprehensive and inclusive approach that protects human rights in diverse cultural contexts. The release of ChatGPT in 2022 marked a true inflection point for artificial intelligence.

Year-over-year, more research is focusing on explainability, bias, and fairness, led by the academic sector. As AI systems develop more impressive capability, they also produce more harm, and with great power comes great responsibility. Over time, debates have tended to focus less and less on possibility and more on desirability,[163] as emphasized in the “Cosmist” and “Terran” debates initiated by Hugo de Garis and Kevin Warwick. A Cosmist, according to Hugo de Garis, is actually seeking to build more intelligent successors to the human species. Traditionally, government has been used by societies to ensure ethics are observed through legislation and policing. There are now many efforts by national governments, as well as transnational government and non-government organizations to ensure AI is ethically applied.

Axel Honneth, a student of Habermas, in turn focused his attention on the topic of recognition, which goes back to Hegel (Honneth, 1996). One of the contemporary, fourth-generation members of the school is Rainer Forst, who has continued the tradition by developing a critical theory of justice and redefining the notions of progress and power, among others. One of its main calls is to protect data, going beyond what tech firms and governments are doing to guarantee individuals more protection by ensuring transparency, agency and control over their personal data. The Recommendation also explicitly bans the use of AI systems for social scoring and mass surveillance. Some argue that AI could help create a fairer criminal judicial system, in which machines could evaluate and weigh relevant factors better than human, taking advantage of its speed and large data ingestion. AI would therefore make decisions based on informed decisions devoid of any bias and subjectivity.