Artificial Intelligence: examples of ethical dilemmas

These frameworks are needed to avoid the deliberate exploitation of the work and creativity of human beings, and to ensure adequate remuneration and recognition for artists, the integrity of the cultural value chain, and the cultural sector’s ability to provide decent jobs. Historically, language models could not be used to relay truthful or factual information about the world. For example, ask a model who was president in 2012, and it could spit out the name of any random politician.

It is easy to

imagine a small drone that searches, identifies, and kills an

individual human—or perhaps a type of human. These are the kinds

of cases brought forward by the Campaign to Stop Killer

Robots and other activist groups. Some seem to be equivalent to

saying that autonomous weapons are indeed weapons …, and

weapons kill, but we still make them in gigantic numbers. On the

matter of accountability, autonomous weapons might make identification

and prosecution of the responsible agents more difficult—but

this is not clear, given the digital records that one can keep, at

least in a conventional war.

Examples of AI ethics

Trying to revert the current state of affairs may expose the first movers in the AI field to a competitive disadvantage (Morley et al., 2019). One should also not forget that points of friction across ethical dimensions may emerge, e.g., between transparency and accountability, or accuracy and fairness as highlighted in the case studies. Hence, the development process of the algorithm cannot be perfect in this setting, one has to be open to negotiation and unavoidably work with imperfections and clumsiness (Ravetz, 1987). On the one hand, a stronger focus on technological details of the various methods and technologies in the field of AI and machine learning is required.

As privileged classes on the edges get caught up on the vortex of negative algorithmic biases, political will must shift toward addressing the challenges of algorithmic oppression for all. For example, companies will be sued – unsuccessfully at first – for algorithmic discrimination. Processes for redress and appeal will need to be introduced to challenge the decisions of algorithms. Douglas Rushkoff, well-known media theorist, author and professor of media at City University of New York, wrote, “Why should AI become the very first technology whose development is dictated by moral principles? Most basically, the reasons why I think AI won’t be developed ethically is because AI is being developed by companies looking to make money – not to improve the human condition. So, while there will be a few simple AIs used to optimize water use on farms or help manage other limited resources, I think the majority is being used on people.

Key findings about Americans and data privacy

Accelerate responsible, transparent and explainable AI workflows across the lifecycle for both generative and machine learning models. Direct, manage, and monitor your organization’s AI activities to better manage growing AI regulations and detect and mitigate risk. These principles and focus areas form the foundation of our approach to AI ethics. To learn more about IBM’s views around ethics and artificial intelligence, read more here. With the emergence of big data, companies have increased their focus to drive automation and data-driven decision-making across their organizations.

• Most notably, Feenberg engaged with this tradition to develop his own critical theory of technology (a.o. Feenberg, 1991).
• 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.
• Parallel to these efforts, UNESCO’s recommendations on AI ethics echo the call for a cohesive global framework, aiming to create consistency in standards across diverse regions and cultures.

In view of AI ethics, approaches that focus on virtues aim at cultivating a moral character, expressing technomoral virtues such as honesty, justice, courage, empathy, care, civility, or magnanimity, to name just a few (Vallor 2016). Those virtues are supposed to raise the likelihood of ethical decision-making practices in organizations that develop and deploy AI applications. Cultivating a moral character, in terms of virtue ethics, means to educate virtues in families, schools, communities, as well as companies.

Dan S. Wallach, a professor in the systems group at Rice University’s Department of Computer Science, said, “Building an AI system that works well is an exceptionally hard task, currently requiring our brightest minds and huge computational resources. Adding the additional constraint that they’re built in an ethical fashion is even harder is ai ethical yet again. “In great part, this requires the passage of laws constraining what corporations can do in pursuit of profit; it also means the government quantifying and paying for public goods so that companies have a profit motive in pursuing them. Corporations and governments are charging evermore expansively into AI development.

The development of decision-making algorithms remains quite obscure in spite of the concerns raised and the intentions manifested to address them. As are the attempt to make the process more inclusive, with a higher participation from all the stakeholders. Identifying a relevant pool of social actors may require an important effort in terms of stakeholders’ mapping so as to assure a complete, but also effective, governance in terms of number of participants and simplicity of working procedures.

There are probably additional discretionary rules of politeness and

interesting questions on when to break the rules (Lin 2016), but again

this seems to be more a case of applying standard considerations

(rules vs. utility) to the case of autonomous vehicles. One more specific issue is that machine learning techniques in AI rely

on training with vast amounts of data. This means there will often be

a trade-off between privacy and rights to data vs. technical quality

of the product. There is no universal, overarching legislation that regulates AI practices, but many countries and states are working to develop and implement them locally. To fill the gap, ethical frameworks have emerged as part of a collaboration between ethicists and researchers to govern the construction and distribution of AI models within society.