Because when you train artificial intelligence on a world soaked in inequity, saturated with bias, and riddled with disinformation, you don’t get fairness. You get injustice at scale. You don’t get objectivity. You get bias with an interface. You don’t get solutions. You get systems that do harm faster, deeper, and with more plausible deniability... See more

Starting with privacy, the main challenge that is linked with privacy violations is the possibility of unfair or otherwise malicious uses of personal data to target individual users. Thus, from our review, it emerges that privacy concerns may be best conceptualised as exposure to risk. Moreover, the types of risk to which privacy violations expose... See more

As noted above, not only the model but also the manner in which it is deployed and in which potential harms are measured and mitigated have the potential to create harmful bias, and a particularly concerning example of this arises in DALL·E 2 Preview in the context of pre-training data filtering and post-training content filter use, which can... See more