Machine-Learning Theory and Its Policy Implications - Naod Abraham

1 Machine-Learning Theory and Its Policy Implications The cause of the behaviour was that each pigeon in the experiment began to associate the delivery of food with whatever particular action it had been performing at the time the food was delivered. [...] ■ Level two: The no-free-lunch theorem states that: if for any hypothesis class and any data generation model chosen where we want to be guaranteed that the hypothesis the learner will pick will have a loss/error lower than , at least of the time, there is no choice but to consider at least half of the points in the domain set. [...] Tasting half of the mangoes in the universe just for the sake of learning to recognize when a mango is tasty is not realistic. [...] One of the guiding principles of AIDA is “transparency.” That is, the public must be provided with enough information to understand the potential impact, capabilities and limitations of the high-impact AI system. [...] However, the following are some of the key factors the government uses to determine if a system is high impact: AI’s severity of potential harm; AI’s scale of use; any evidence of impact on human rights; and any evidence of harm to the health and safety of individuals.