CIODigital Disruption

What Factors Lead to Artificial Intelligence Decision-Making?

Leveraged reasonably, artificial intelligence can be a boon. However, companies are more inclined to look at it from a business perspective of cost efficiency and productivity. In this article at Aeon, Paul Boddington discusses factors that lead to artificial intelligence decision-making.

Optimizing Artificial Intelligence

Currently, AI can only perform limited tasks. While it needs training with several photos to identify a cat, a human child can recognize with just one glance. However, the results of its influence are various. While it can improve certain things, it can also reduce human involvement in others. We lapped up technology fast but did not think about its cultural and social impacts. Though people are worried about data privacy, sharing the same on social media have increased tenfold. As per recent researches, people’s thought process is conflicting.

We hardly realize how technology has changed our thoughts, communication, and outlook. Printing press and telephone have changed how we interacted prior to that. Algorithms will shape our thinking and behavior too. Find out the factors that lead to artificial intelligence decision-making:

Interconnected Memory: Oxford Mobile Robotics Group information engineer Paul Newman explains artificial decision-making with an example. When you run into a car accident, neither your friend nor you learn a lot. AI machines share such information with each other.They retrieve information from a common knowledge pool and analyze data faster. This prevents an AI-driven car to repeat mistakes.

Decision Uniformity or Monopoly: Artificial intelligence go through myriads of data and catch differences that human eyes cannot. However, making AI the primary source of diagnosis can lead to irretrievable errors. Stanford University School of Medicine’s Danton Char and his co-authors expressed concerns in “The New England Journal of Medicine”. Dependency on AI diagnosis can affect independent thinking. The intelligent machines can shut down care options for terminally ill patients. Machine learning can provoke medical practitioners to work on their professional targets rather than care for patients. Data will be more important than critical thinking, so off-course cases will receive the wrong treatment.

Accurate Justice or the Emotional Chance: Algorithms are already used in the U.S. court to help in the decision-making process. The problem is, if verdicts of similar cases are biased, artificial intelligence will make the same biases. Sometimes judge and jury have acquitted the defaulter to give a second chance in life. So, in this case, more than accurate judgment, human emotions are necessary. In the future, AI might help to come to a conclusion, but it will never replace human judges.

Filtering Out Hate or Freedom of Speech: The Anti-Defamation League (ADL) and D-Lab are producing an online hate index (OHI) based on ML. While this can solve part of the problem, there is no guarantee that it will not discriminate. Artificial intelligence can ban you from a social account for use of a swearword during a friendly banter. This also is sabotaging your freedom of thought.

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