The Ethics of AI Ethics: An Evaluation of Guidelines Minds and Machines

So, one form of

bias is a learned cognitive feature of a person, often not made

explicit. The person concerned may not be aware of having that

bias—they may even be honestly and explicitly opposed to a bias

they are found to have (e.g., through priming, cf. Graham and Lowery

2004). This does not mean that we expect an AI to

“explain its reasoning”—doing is ai ethical so would require far

more serious moral autonomy than we currently attribute to AI systems

(see below

§2.10). It may seem counterintuitive to use technology to detect unethical behavior in other forms of technology, but AI tools can be used to determine whether video, audio, or text (hate speech on Facebook, for example) is fake or not.

This issue may be further exacerbated by the offer of new services of auto-ML (Chin, 2019), where the entire algorithm development workflow is automatised and the residual human control practically removed. In addition to the endorsement of virtue ethics in tech communities, several institutional changes should take place. I argue that the prevalent approach of deontological AI ethics should be augmented with an approach oriented towards virtue ethics aiming at values and character dispositions.

Biased AI

The study’s goal was to understand the teachers’ perspectives on ethics around AI. Teachers were asked to rate how much they agreed with different ethical ideas and to rate their willingness to use generative AI, like ChatGPT, in their classrooms. The center advances the USC Frontiers of Computing initiative, a $1 billion-plus investment to promote and expand advanced computing research and education across the university in a strategic, thoughtful way. Google has spent the past year laying off thousands of workers and streamlining its operations to more quickly deliver advances to users and focus around a few AI initiatives.

• The rapid rise in artificial intelligence (AI) has created many opportunities globally, from facilitating healthcare diagnoses to enabling human connections through social media and creating labour efficiencies through automated tasks.
• The conversation around AI ethics is also important to appropriately assess and mitigate possible risks related to AI’s uses, beginning the design phase.
• Gary A. Bolles, chair for the future of work at Singularity University, responded, “I hope we will shift the mindset of engineers, product managers and marketers from ethics and human centricity as a tack-on after AI products are released, to a model that guarantees ethical development from inception.
• It appears

  that the success of “being cared for” relies on this

  intentional sense of “care”, which foreseeable robots

  cannot provide.

The notion of “artificial intelligence” (AI) is understood

broadly as any kind of artificial computational system that shows

intelligent behaviour, i.e., complex behaviour that is conducive to

reaching goals. In particular, we do not wish to restrict

“intelligence” to what would require intelligence if done

by humans, as Minsky had suggested (1985). This means we

incorporate a range of machines, including those in “technical

AI”, that show only limited abilities in learning or reasoning

but excel at the automation of particular tasks, as well as machines

in “general AI” that aim to create a generally intelligent

agent. Some technologies, like nuclear power, cars, or plastics, have caused

ethical and political discussion and significant policy efforts to

control the trajectory these technologies, usually only once some

damage is done. In addition to such “ethical concerns”,

new technologies challenge current norms and conceptual systems, which

is of particular interest to philosophy. Finally, once we have

understood a technology in its context, we need to shape our societal

response, including regulation and law.

What the data says about Americans’ views of artificial intelligence

Artificial intelligence (AI) is the branch of computer science that deals with the simulation of intelligent behaviour in computers as regards their capacity to mimic, and ideally improve, human behaviour. To achieve this, the simulation of human cognition and functions, including learning and problem-solving, is required (Russell, 2010). This simulation may limit itself to some simple predictable features, thus limiting human complexity (Cowls, 2019). Despite the downsides, in less public discourses and in concrete practice, an AI race has long since established itself. Competitors are seen more or less as enemies or at least as threats against which one has to defend oneself.

Accordingly, the “male way” of thinking about ethical problems is reflected in almost all ethical guidelines by way of mentioning aspects such as accountability, privacy or fairness. In contrast, almost no guideline talks about AI in contexts of care, nurture, help, welfare, social responsibility or ecological networks. In AI ethics, technical artefacts are primarily seen as isolated entities that can be optimized by experts so as to find technical solutions for technical problems. What is often lacking is a consideration of the wider contexts and the comprehensive relationship networks in which technical systems are embedded. The list of ethics guidelines considered in this article therefore includes compilations that cover the field of AI ethics as comprehensively as possible. To the best of my knowledge, a few preprints and papers are currently available, which also deal with the comparison of different ethical guidelines (Zeng et al. 2018; Fjeld et al. 2019; Jobin et al. 2019).

In an ideal world, Maskey said opt-in for users deciding to share their personal data, rather than opt-out, would be the standard, and ideally, people would be able to easily access and research all data that’s collected about them. The straightforward answer would be to align a business’s operations with one or more of the dozens of sets of AI ethics principles that governments, multistakeholder groups and academics have produced. Constitutive power, finally, refers to the views or discussions of power that focus not on the oppressive character of power, but on the ways in which those subjected to power are also shaped by it.

Should we still pursue autonomous vehicles, or do we limit the integration of this technology to create only semi-autonomous vehicles which promote safety among drivers? The jury is still out on this, but these are the types of ethical debates that are occurring as new, innovative AI technology develops. AI ethics is a set of guiding principles designed to help humans maximize the benefits of artificial intelligence and minimize its potential negative impacts. These principles establish ‘right’ from ‘wrong’ in the field of AI, encouraging producers of AI technologies to address questions surrounding transparency, inclusivity, sustainability and accountability, among other areas. Forst writes that “the question of power, qua social and political power that shapes collective processes, is central to justice” (Forst, 2015, 8). When we speak of justice, we are referring to what we consider to be acceptable power relations or systems in society.

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The IEEE’s Ethics of Autonomous Systems initiative aims to address ethical dilemmas related to decision-making and the impact on society while developing guidelines for the development and use of autonomous systems. In particular in domains like artificial intelligence and robotics, the Foundation for Responsible Robotics is dedicated to promoting moral behavior as well as responsible robot design and use, ensuring that robots maintain moral principles and are congruent with human values. And in cases where ethics is integrated into institutions, it mainly serves as a marketing strategy. Furthermore, empirical experiments show that reading ethics guidelines has no significant influence on the decision-making of software developers.