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Generative Artificial Intelligence, Large Language Models, Diffusion Models, AI Ethics, Algorithmic Bias, Fairness, Representational Harm, Disinformation, Deepfakes, Informational Integrity, Intellectual Property, Copyright Infringement, Model Accountability, Transparency, Black-Box Models, Data Privacy, Workforce Disruption, Creative Industries, AI Governance, AI Regulation, Digital Literacy, Model Alignment, Watermarking, Bias Mitigation |
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The advent of Generative Artificial Intelligence (GenAI) is a paradigm shift of historic proportions for computational science and its workings when applied to human endeavours. This class of artificial intelligence, which includes architectures like Large Language Models (LLMs) as well as diffusion-based generators of images, has shown some prowess in generating consistent, high fidelity and often seemingly novel forms of text, image, audio, and code. This ability is a departure from previous AI systems which were mostly analytical or discriminative in nature. GenAI by contrast, is essentially creative and synthesizing and allows machines to produce what was once only a realm of fluid human cognition and artistry. The introduction of publicly accessible tools like ChatGPT, DALL-E, Midjourney and Stable Diffusion has helped catalyse a democratisation of creative power by providing unprecedented tools for boosting productivity, speeding up scientific discovery, encouraging artistic expression and personalising education.
However, this transformative potential is cast in darkness by a complex and pressing constellation of ethical problems which threaten to undo its benefits. The same qualities that make GenAI so powerful make it a powerful weapon for considerable harm. This paper offers a broad discussion and analysis of the major ethical problems posed by the rapid spread of GenAI. We argue that the ethics of the situation does not represent a mere continuation of pre-existing concerns of AI ethics but is qualitatively different and more extreme given the unique operational characters of the technology.
The first important issue examined is the amplification of societal bias and the compromise of fairness. GenAI models are trained on large datasets that are safely on the scale of the internet; the data inevitably embeds the prejudices and stereotypes and historical inequities within its body of data. As opposed to discriminative models that can perpetuate bias within decision making processes (such as for hiring or loan applications), Generative AI actively synthetically spreads these biases in the outputs. For example, a model could produce pictures of professionals in roles that perpetuate and reinforce gender or racial stereotypes or produce text with unspoken cultural biases. This causes representational harm, where groups that are not represented sufficiently are marginalised and negative stereotypes are solidified, and allocation harm, when these biased outputs are fed into automated systems that have consequences for people's life opportunities.
The second great area of concern is the informational integrity threat posed by the proliferation of disinformation. The ability of GenAI to create highly plausible and mass customised text, audio, and visual media on an unprecedented scale and speed has become a powerful challenge to the integrity of public discourse. Malicious actors can use these tools to create impressive fake news articles, create convincing social media personas to support astroturfing campaigns, and create hyper-realistic deep fake videos for being served in political manipulation, financial fraud or personal defamation. This not only speeds up the devaluation of trust in traditional media and democratic institutions but also gives rise to a "liar's dividend", where the operation of the most convincing synthetic media means that any inconvenient genuine evidence can be dismissed as "fabricated" by bad faith actors, creating a climate of general scepticism and a state of epistemological chaos.
Third, we investigate innovative and open questions of originality and intellectual property (IP). The very operational paradigm of GenAI presents questions that trigger legal and ethical concerns that the current IP frameworks are ill-equipped to address. The process of training them is also coming under question, as models are generally trained on terabytes of copyrighted data, ranging from books, articles and artworks, scraped from the web without specific consent or payment to the copyright holder, resulting in a number of high-profile lawsuits citing mass copyright infringement. Furthermore, the nature of the output results in an accountability vacuum: who, if anyone, can own a generated asset? Is the user who created the prompt, the team behind the model (the developers), or the machinery that creates the model (the neural network) at fault, because it's results that are in the public domain by default that there's been no real human authorship? This uncertainty about the law chokes innovation and commercial application.
Beyond these specific issues, in the paper as a whole, the research looks at the wider societal and economic consequences, such as the potential of disruption in creative professions and knowledge work professions overall, which will create challenges on workforce transition and economic equality. We also consider the "black box" problem of accountability and transparency where the usually impenetrable nature of complex models and thus where it's hard to attribute responsibility when their models cause harm, privacy risks where models have been shown to memorize and private sensitive personal data from their training sets.
At the centre of our argument is the thesis that these ethical dilemmas are critically worsened through three inherent features of GenAI: its unprecedented scaling and speed, by which it can overcome conventional content moderation and fact checking systems; its democratisation of access, which lowers the threshold for misuse by techno physically unsophisticated actors; and the probabilistic nature and powerful faithfulness of GenAI content, leading to confident hallucinations of false information and making its outputs terrible to tell apart from the work of human hand.
In conclusion, this paper maintains that to work through this "Prometheus Dilemma"-i.e., to harness the fire of GenAI without being consumed by it-in the process, this multi-stakeholder approach to governance requiring proactivity is necessary. We propose that a siloed approach is bound to fail. Instead a synergistic strategy is not just recommended, but necessary. This must include technical mitigations such as robust bias detection, watermarking and model alignment techniques; evolved legal and regulatory solutions that amend IP law, create clear frameworks of liability and enforce them; and societal and educational strategies focused on increasing the digital and AI literacy of the population. Only through the concerted and cooperative efforts of technologists, policymakers, ethicists, educators, and civil society can we hope to shape the future of Generative AI into one that is responsible, equitable, and ultimately beneficial to all of humanity. The time of establishing these critical guardrails is the present (while technology is in the nascent stages of incorporating into the global social fabric). |