From this summary, list three ways in which artificial intelligence systems show behavior misaligned human values, covered by Brian Christian in the three sections of his book "The Alignment Problem"?
The book is divided into three sections: Prophecy, Agency, and Normativity. Each section covers researchers and engineers working on different challenges in the alignment of artificial intelligence with human values.

Prophecy
In the first section, Christian interweaves discussions of the history of artificial intelligence research, particularly the machine learning approach of artificial neural networks such as the Perceptron and AlexNet, with examples of how AI systems can have unintended behavior. He tells the story of Julia Angwin, a journalist whose ProPublica investigation of the COMPAS algorithm, a tool for predicting recidivism among criminal defendants, led to widespread criticism of its accuracy and bias towards certain demographics. One of AI's main alignment challenges is its black box nature (inputs and outputs are identifiable but the transformation process in between is undetermined). The lack of transparency makes it difficult to know where the system is going right and where it is going wrong.

Agency
In the second section, Christian similarly interweaves the history of the psychological study of reward, such as behaviorism and dopamine, with the computer science of reinforcement learning, in which AI systems need to develop policy ("what to do") in the face of a value function ("what rewards or punishment to expect"). He calls the DeepMind AlphaGo and AlphaZero systems "perhaps the single most impressive achievement in automated curriculum design." He also highlights the importance of curiosity, in which reinforcement learners are intrinsically motivated to explore their environment, rather than exclusively seeking the external reward.

Normativity
The third section covers training AI through the imitation of human or machine behavior, as well as philosophical debates such as between possibilism and actualism that imply different ideal behavior for AI systems. Of particular importance is inverse reinforcement learning, a broad approach for machines to learn the objective function of a human or another agent. Christian discusses the normative challenges associated with effective altruism and existential risk, including the work of philosophers Toby Ord and William MacAskill who are trying to devise human and machine strategies for navigating the alignment problem as effectively as possible.
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In the first section, Prophecy, the author talks about predictive models making decisions that are difficult to examine and may become discriminative or unfair.

In the second section, Agency, about reinforcement learning systems, despite some remarkable achievements, are guided nearly exclusively by their objective function, not so much by curiosity to discover the world.

In the third section, Normativity, inverse reinforcement learning techniques face the challenge of figuring out the objective function of humans.
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