Ethical Considerations in Generative AI: A Leadership Imperative.

Leadership is the critical force behind successful organizations. To create vital and viable organizations, leadership is necessary to develop a new vision of what they can be and then mobilize the organization to change towards that vision. –Warren Bennis and Burt Nanus, Leaders, Strategies for Taking Charge, 2007

In the age of rapid technological advancements, the interplay of ethics and artificial intelligence continues to be a paramount concern, especially to the leadership of organisations. Concerns such as the depths of ethical issues in generative AI.

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What is Generative AI

Generative AI is artificial intelligence (AI) that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request. Generative AI relies on sophisticated machine learning models called deep learning models—algorithms that simulate the learning and decision-making processes of the human brain.

Generative AI can be used for a range of activities such as creating software code, facilitating drug development and targeted marketing, but also misused for scams, fraud, political disinformation, forged identities and more.

The Ethical Dilemma of Generative AI

Unraveling the intricacies surrounding ethical issues in generative AI involves putting side-by-side the moral implications of AI with its generative capabilities. Ethical concerns within generative AI revolve around the proliferation of AI systems which produces content that blurs the line between authentic and artificial ones, raising significant questions about the ethical responsibilities of the technology's creators and users.

Let’s try to understand the ethical dilemma of Generative AI with the help of some researched facts on one of its use cases from among many – image manipulation:

• A study by the University of Warwick found that individuals failed to detect approximately 35% of manipulated images depicting real-world scenes.
• Another study indicates that individuals possess only a 50% probability of accurately discerning whether AI-generated content is real or fake.
• More recently, the proportion of deepfakes in North America has more than doubled from 2022 to Q1 2023, specifically from 0.2% to 2.6% in the U.S. and from 0.1% to 4.6% in Canada.

AI-synthesized text, audio, images, and videos are being used for the unauthorized sharing of personal content, financial scams, and spreading false information. Currently, the rate of AI-generated images produced daily surpasses 2 million, which means the potential for harm is also rapidly escalating. This has raised serious ethical concerns about AI among business leaders, the government, and people around the world. [Data Source]

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Below are the critical ethical implications that businesses dabbling in generative AI must navigate, alongside some potential pitfalls.

1. Misinformation and Deepfakes: Generative AI's capacity to produce content that blurs the lines between reality and misrepresentation is alarming. From false news reports to manipulated videos, fake social media profiles, these creations can distort and disrupt public perception and social harmony, fuel propaganda, and detrimentally impact both individuals and organizations. Also, the ability to generate personalized content based on individual data introduces new opportunities for manipulation, such as AI voice-cloning scams, and poses ongoing challenges in fake detection. For instance, there have been cases where deepfakes were used to make it seem like politicians said or did things they never actually said or did, or a deepfake video purportedly showing a major corporation CEO making controversial remarks, leading to stock price plummeting overnight.
2. Bias and Discrimination: AI that continuously perpetuates or even exaggerates biases can draw public rage, legal repercussions, and brand reputational damage. Take for example a facial recognition software, which, when biased, might wrongly identify individuals leading to potential litigation or corporate reputation disasters. An instance of bias was when Amazon, in 2018, had to discard an AI recruiting tool because it exhibited bias against women. Trained on resumes submitted over ten years, the tool favored male candidates as most resumes in its dataset were from men. Organizations need to prioritize diversity in training of datasets and commit to periodic audits to check for unintended biases. Companies can initiate partnerships with AI organizations, ensuring that their generative models undergo rigorous bias checks and external audits.
3. Copyright and Intellectual Property: When generative AI tools create images, codes, or videos, they might refer to sources that are inaccurate in their training data, potentially violating intellectual property or copyrights. Consequently, an ethical concern surrounding Generative AI revolves around uncertainties regarding the authorship and copyright of AI-generated content, determining ownership rights, and permissible generative AI use. For instance, if a generative AI tool produces music akin to a copyrighted song in the music industry, it could lead to expensive lawsuits and public outcry. Litigations have emerged, like Andersen v. Stability AI. in late 2022, where artists sued AI platforms for using their work without permission. Ensure that training content is licensed and transparently outlines how generated content was produced.
4. Privacy & Data Security: Generative AI models, particularly those trained on personal data, pose privacy risks. This unauthorized use of data or the generation of weirdly accurate synthetic profiles is of significant concern. A breach of user privacy or data misuse can trigger legal actions and erode trust. Consider an AI trained on personal banking profile histories inadvertently generating a profile that, though synthetic, closely resembles a real bank customer, leading to privacy concerns and potential violation of the customer right and the provision of the Nigeria Data Protection Act 2023 which provides a legal framework for the protection of personal information and also establishes the Nigeria Data Protection Commission for the regulation of the processing of personal information.

Implication for Businesses

Becoming aware and accepting the reality of ethical dilemma is where it starts, and also recognizing and understanding the ethical hazards connected with generative AI. Once they are identified, proactively put policies, processes and strategies in place that promotes responsible use. Lastly, the business must champion transparency and foster a culture of ethical AI usage both within and outside the organization.

Beyond the expectation of the society and even the ethical implications, businesses face other tangible risks if they turn a blind eye to these issues. They could suffer brand reputational loss, user trust erosion and financial instability. Ignoring the ethical aspects of generative AI is not just a moral oversight; it's a business risk that could cost organisations dearly.

About Adenrele Onikosi

Adenrele Onikosi is passionate about leadership excellence. He empowers executives to redefine their leadership journeys, emphasizing human-centric approaches, as enabler of business sustainability.