Mubarak Hussain, Indian Institute of Technology Dharwad

For a long time, researchers of Artificial Intelligence (AI) and futurists have hypothesized that the developed Artificial General Intelligence (AGI) systems can execute intellectual and behavioral tasks similar to human beings. However, there are two possible concerns regarding the emergence of AGI systems and their moral status, namely: 1) is it possible to grant moral status to the AGI-enabled robots similar to humans? 2) if it is (im)possible, then under what conditions do such robots (fail to) achieve moral status similar to humans? To examine the possibilities, the present study puts forward a functionality argument, which claims that if a human being and an AGI-enabled robot have similar functionality, but different creative processes, they may have similar moral status. Furthermore, the functionality argument asserts that an entity’s (a human being or an AGI-enabled robot) creation/production from carbon or silicon or its brain’s utilization of neurotransmitters or semiconductors does not carry any significance. Rather, if both entities have similar functionality, they may have similar moral status, which implies that the AGI-enabled robot may achieve human-like moral status if it performs human-like functions.

This paper argues Moral Turing Test (MTT) developed by Allen et al. for evaluating morality in AI systems is designed inaptly. Different versions of the MTT focus on the conversational ability of an agent but not the performance of morally significant actions. Arnold and Scheutz also argue against the MTT and state that without focusing on the performance of morally significant actions, the MTT is insufficient. Morality is mainly about morally relevant actions because it does not matter how good a person is at conversing about morally relevant actions. When discussing morality, we consider an agent’s ability to perform specific actions in a morally given situation. We show that Allen et al. do not take into account the distinction between the performance ofthe moral attribution and the performance of the morally relevant action. This distinction gives a robust account of assessing the morality of an AI system in the MTT

This paper examines whether machines (algorithms/programs/ AI systems) are apt subjects for praise or blame for some actions or performances. I consider "Move 37" of AlphaGo as a case study. DeepMind’s AlphaGo is an AI algorithm developed to play the game of Go. The AlphaGo utilizes Deep Neural Networks. As AlphaGo is trained through reinforcement learning, the AI algorithm can improve itself over a period of time. Such AI models can go beyond the intended task and perform novel and unpredictable functions. There is a surprise element associated with "Move 37". "Move 37" not only surprises the Go players, the programmers, but also whoever is informed of this unpredicted move. Does someone or something deserve praise or blame for the surprise? If so, who or what deserves the praise or blame for "Move 37"? The programmer cannot be praised for "Move 37", which is either surprising or was not intended or imagined at all. At the same time, would we accept that neither the algorithm deserves praise for the unpredicted move that the algorithm allowed the program to make? From this, would we accept that since neither the programmer nor the algorithm/AI system deserves the praise, there is such a good or exciting move for which no one or nothing could be praised? Would we say this unpredictable move is a move for which no one deserves praise or blame? Wouldn’t there be at least a few who were surprised by the unpredictable move? Should we say that for this pleasant surprise, no one deserves praise? Nonetheless, for us, specifically regarding the particular unpredictable move, we firmly find it counterintuitive to say that there is an exciting move for which no one deserves praise. The surprise element is the result of the property that belongs to the algorithm. It seems quite difficult for us to accept that no one deserves praise for "Move 37" or for similar moves. Therefore, someone or something deserves praise which is a matter of scrutiny.

Using speech act theory from the Philosophy of Language, this paper attempts to develop an ethical framework for the phenomenon of speech processing. We use the concepts of the illocutionary force and the illocutionary content of a speech act to explain the ethics of speech processing. By emphasizing the different stages involved in speech processing, we explore the distinct ethical issues that arise in relation to each stage. Input, processing, and output are the different ethically relevant stages under which a spoken item or a speech navigates within the range of speech-processing modules. Employing the illocutionary force-content distinction, we specify and characterize the input-related ethical issues, the output-related ethical issues, and the processing-related ethical issues involved in speech processing. Together with illocutionary force-content distinction, we employ the data-information distinction to characterize the stage-wise ethical issues in the phenomenon of speech processing as the ethics of collecting (speech) data, the ethics of contextualizing (speech) data/information, and the ethics of releasing the con-textualized information (processed speech). Immediate ethical issues that arise from the range of speech processing modules are distinguished from distant ethical issues. We also indicate the nature of ethical issues that arise from Speaker Independent speech technologies.