Mediating artificial intelligence developments through negative and positive incentives

The field of Artificial Intelligence (AI) is going through a period great expectations, introducing certain level anxiety in research, business and also policy. This further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, belief this may detrimental as some stake-holders will feel obliged to cut corners on safety precautions, ignore societal consequences just “win”. Starting from baseline model describes broad class technology races where winners draw significant benefit compared others (such advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) negative (punishments) incentives beneficially influence the outcomes. We uncover conditions which punishment either capable reducing development speed unsafe participants has capacity reduce innovation over-regulation. Alternatively, show that, several scenarios, rewarding those follow measures increase while ensuring safe choices. Moreover, latter regimes, rewards do not suffer issue over-regulation case for punishment. Overall, our findings provide valuable insights into nature kinds regulatory actions most suitable improve compliance contexts both smooth sudden technological shifts.