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Briefly describe machine ethics.
Focus on the ethical issues surrounding machine ethics.
Please give suggestions on how to resolve these problems.
The following is a brief introduction to the topic:
Machine Ethics, a new field of study that seeks to create machines capable of evaluating the ethical implications and acting accordingly.
The goal of machine ethics is to ensure that machines behave ethically towards humans and other machines.
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It is difficult to teach morality to computers because we cannot objectively communicate morality using measurable metrics for the computer to easily process. It is therefore a challenge to quantify societal expectations in a way that can be accepted. Humans tend to use contextual intuition in moral dilemmas instead of complex quantitative calculations. In contrast, machines require explicit metrics and objectives that are easily measured and optimized.
Fear of possible autonomous machines. Humans are afraid of whether autonomous intelligent machines behave ethically. AI researchers may be allowed to create autonomous intelligent machines if they can build safeguards to prevent unethical behavior.
Ethical Relativism : One philosophical question about the viability of machine ethics is whether or not there’s a universally acceptable standard. Ethics is viewed as a relative concept, either by society or individual. It is unlikely that a universal code of ethics will be developed. The challenge then is to make sure machine ethics are in line with the culture where they work.
Doctrine double effect. According to doctrine double effect, intentionally inflicting damage is wrong even when it’s good. The doctrine of double effects will raise issues when encoding morals into machines or teaching them to harm intentionally to solve a potential dilemma.
Stereotyping is a real threat. Individuals or social groups can be stereotyped based on the preferences they have. A machine that is artificially intelligent can end up reproducing social prejudices, and perpetuating racism based on gender, race or religion.
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Researchers and ethicists in AI need to define ethical behavior explicitly as quantifiable values. To arrive at moral standards, they must also understand ethical relativism.
Collecting and analyzing relevant data: The engineers must collect sufficient information on ethical issues to train AI algorithms. It is important to have enough data that are unbiased in order to properly train AI algorithms.
Transparency in AI is key to making AI more effective.
The policymakers must implement guidelines to make AI ethics decisions more transparent. This is especially true when it comes to metrics and ethical outcomes.
The conclusion of the article is:
It is not possible to assume that machines are morally capable by default. They must be taught what morality means, and how to measure it.
Inaction can have a huge impact on the lives of millions of people.
Academics, engineers, and policymakers must therefore develop a rapid response to this new field.
