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Council Post: Exploring the Ethical Implications of Generative AI – Bias, Deepfakes, and Misinformation

Generative AI has the potential to improve human creativity and productivity in a variety of areas, including picture synthesis and text production. However, the advent of the Generative AI era has completely reshaped our perceptions of machine capabilities.

Until a few years ago, the notion of a machine generating art, crafting narratives, or comprehending intricate data like medical and legal documents seemed almost inconceivable. It resided mostly within the realm of science fiction, distant from practical reality. With the development of generative artificial intelligence (AI), which enables machines to produce extraordinarily realistic and imaginative material, several industries have undergone a revolution. Generative AI has the potential to improve human creativity and productivity in a variety of areas, including picture synthesis and text production. However, the advent of the Generative AI era has completely reshaped our perceptions of machine capabilities.

However, when Sam Altman, the CEO of OpenAI, warns us about language model risks, the risk is real, thus it is imperative to weigh its advantages with caution and use this powerful technology properly.The article explores the ethical implications of generative AI, focusing on three critical aspects: bias, deep fakes, and misinformation. It delves into the potential risks and challenges posed by these technologies, raising awareness about the need for responsible AI development and usage.

Bias in Generative AI

Generative AI models learn from vast datasets, which can inadvertently perpetuate societal biases present in the data. This can lead to biased outputs in image, text, and video generation, reinforcing harmful stereotypes and discriminatory content. Understanding the root causes of bias in generative AI and implementing measures to mitigate it are essential to promote fairness and inclusivity in AI technologies. 

Chatbots are not immune to the preconceptions of their developers, despite their rising popularity and capacity for individualised customer care. The issue comes with the data sets that were used to train these chatbots since they may reveal the preconceptions of their designers or reinforce pre existing prejudices in society. Research cites several instances of chatbots that frequently made sexist jokes and recommended higher-paying employment to males over women, among other examples of chatbots that display gender and racial biases. It is crucial to make sure that generative AI is not feeding negative preconceptions or leading to unjust treatment of people as it gets more sophisticated and pervasive. 

The Rise of Deepfakes and Their Impact

Deep fake technology has been present since the 1990s, when speech was manipulated using it in the film industry. Deep fakes have been on the rise since the development of artificial intelligence, and not for good reasons. According to Deeptrace, a cybersecurity firm, the quantity of online deepfake videos increased in 2019 and reached around 15,000 in less than a year.  The proliferation of deepfake technology raises concerns about the manipulation of visual content. Deepfakes can be used to create convincing fake videos or images of individuals, potentially causing severe reputational damage, spreading misinformation, and fueling distrust. 

After Amazon demonstrated the idea of Alexa speaking with the voice of a deceased loved one, voice cloning gained attention a few months ago. The drawback of this technology is that con artists could profit from it. They may readily mimic voices to their advantage, such as appearing to be members of the family or high-ranking officials. Perhaps the next increase in cybercrime will be driven by fake audio.

Analyzing the impact of deep fakes on society, discussing detection methods, and exploring legal and ethical considerations can help safeguard against the malicious use of this technology. Many firms have approached deep fakes differently, notably Truepic, which has garnered $26 million from M12, Microsoft’s venture arm. Instead of concentrating on finding fraudulent content, they track the validity of the content while it is being collected.

Misinformation and the Role of Generative AI

Internet searches using ChatGPT are different from those with Google. Instead, it creates answers to questions by anticipating potential word combinations using a vast collection of web data. Generative AI has been demonstrated to have some serious flaws even if it has the potential to increase productivity. It could spread false information. Hallucinations, an umbrella word for making things up, might result from it. Generative AI can be harnessed to generate highly realistic fake news and other forms of misinformation. This poses a significant threat to public discourse and challenges the integrity of information in the digital age.

With generative AI’s rise, making credibility decisions in the midst of abundant digital information becomes more complex. Biased data in AI training can lead to conflicting or erroneous outcomes. Deliberate misinformation creation through AI compounds the issue, enabling rapid spread of false scientific content. The absence of proper referencing in AI-generated material raises concerns about fabricated sources, potentially misleading readers seeking authoritative information. There’s also the risk of outdated information, as AI systems lack post-training awareness. Amidst rapid AI advancement and limited transparency, ensuring future AI-generated scientific accuracy demands robust measures.

Strategy to combat

Provenance Verification: An effective approach to counter misinformation involves enhancing transparency regarding content origin and its journey. Initiatives like the Content Authenticity Initiative led by Adobe are dedicated to assisting image creators in establishing content authenticity. Microsoft has taken a similar stance by incorporating metadata into content developed through generative AI tools. Google also plans to expand the disclosure of information related to the images indexed in its search engine.

Regulatory Measures: Legislation can play a crucial role in combating misinformation. While such efforts need to respect the boundaries of free speech, there’s value in enforcing rules that mandate the identification of funders behind political advertisements or prohibit harmful practices like deepfake-based harassment and extortion.

AI-Powered Algorithms: Paradoxical as it might seem, some experts advocate for the utilization of AI itself as a weapon against machine-generated misinformation. Despite the challenges, deploying AI to detect such content represents a dynamic approach, given the overwhelming volume of AI-generated misinformation that human moderators can hardly keep up with.

Promoting Media Literacy: Another strategic avenue involves empowering individuals with the skills to critically evaluate information. By cultivating media literacy, people can become more discerning consumers of content. However, achieving this requires a coordinated effort, substantial investment, and overcoming opposition from parties that may benefit from an uninformed populace.

In conclusion, the ethical implications of generative AI, encompassing bias, deep fakes, and misinformation, demand immediate attention from researchers, policymakers, and technology developers. Addressing these concerns requires a multi-faceted approach involving transparency, accountability, and ongoing collaboration among stakeholders. By actively considering the ethical dimensions of generative AI, we can strive to create a future where AI technologies are used responsibly, promoting societal well-being and safeguarding against potential harms.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.

Rathnakumar is the Product Lead Cloud and AI at Netradyne. He has over a decade of experience in the field of Data Science and AI. He has played a significant role in building SAAS and PAAS products across the globe. Additionally, he has founded and co-founded multiple startups, going through the journey of launching, fundraising, and acquisitions.

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