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\n\t\t\t\t\t\t\tTracey Meares<\/span> \n\t\t\t\t\t\t\tSudhir Venkatesh<\/span> \n\t\t\t\t\t\t\tMatt Katsaros<\/span> The much-touted arrival of generative AI has reignited a familiar debate about trust and safety: Can tech executives be trusted to keep society\u2019s best interests at heart?<\/p>\n Because its training data is created by humans, AI is inherently prone to bias and therefore subject to our own imperfect, emotionally-driven ways of seeing the world. We know too well the risks, from reinforcing discrimination and racial inequities to promoting polarization.<\/p>\n OpenAI CEO Sam Altman has requested our \u201cpatience and good faith\u201d<\/a> as they work to \u201cget it right.\u201d<\/p>\n For decades, we\u2019ve patiently placed our faith with tech execs at our peril: They created it, so we believed them when they said they could fix it. Trust in tech companies continues to plummet, and according to the 2023 Edelman Trust Barometer, globally 65% worry <\/a>tech will make it impossible to know if what people are seeing or hearing is real.<\/p>\n It is time for Silicon Valley to embrace a different approach to earning our trust \u2014 one that has been proven effective in the nation\u2019s legal system.<\/p>\n Grounded in social psychology, procedural justice is based on research showing that people believe institutions and actors are more trustworthy and legitimate when they are listened to and experience neutral, unbiased and transparent decision-making.<\/p>\n Four key components of procedural justice are:<\/p>\n Using this framework, police have improved trust and cooperation in their communities and some social media companies are starting to use these ideas to shape governance and moderation<\/a> approaches.<\/p>\n Here are a few ideas for how AI companies can adapt this framework to build trust and legitimacy.<\/p>\n As UCLA Professor Safiya Noble<\/a> argues, the questions surrounding algorithmic bias can\u2019t be solved by engineers alone, because they are systemic social issues that require humanistic perspectives \u2014 outside of any one company \u2014 to ensure societal conversation, consensus and ultimately regulation \u2014 both self and governmental.<\/p>\n In \u201cSystem Error: Where Big Tech Went Wrong and How We Can Reboot,\u201d<\/a> three Stanford professors critically discuss the shortcomings of computer science training and engineering culture for its obsession with optimization, often pushing aside values core to a democratic society.<\/p>\n In a blog post, Open AI says it values societal input<\/a>: \u201cBecause the upside of AGI is so great, we do not believe it is possible or desirable for society to stop its development forever; instead, society and the developers of AGI have to figure out how to get it right.\u201d<\/p>\n However, the company\u2019s hiring page and founder Sam Altman\u2019s tweets<\/a> show the company is hiring droves of machine learning engineers and computer scientists because \u201cChatGPT has an ambitious roadmap and is bottlenecked by engineering.\u201d<\/p>\n Are these computer scientists and engineers equipped to make decisions that, as OpenAI has said, \u201cwill require much more caution than society usually applies to new technologies<\/a>\u201d?<\/p>\n Tech companies should hire multi-disciplinary teams that include social scientists who understand the human and societal impacts of technology. With a variety of perspectives regarding how to train AI applications and implement safety parameters, companies can articulate transparent reasoning for their decisions. This can, in turn, boost the public\u2019s perception of the technology as neutral and trustworthy.<\/p>\n Another element of procedural justice is giving people an opportunity to take part in a decision-making process. In a recent blog<\/a> post about how OpenAI is addressing bias, the company said it seeks \u201cexternal input on our technology\u201d pointing to a recent red teaming exercise, a process of assessing risk through an adversarial approach.<\/p>\n
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A procedural justice approach to trust and legitimacy<\/h2>\n
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Build the right team to address the right questions<\/h2>\n
Include outsider perspectives<\/h2>\n