Artificial Intelligence - Ethics & Bias

Artificial Intelligence is the technology that has the ability to replicate human actions. Machines learn from past experiences, make decisions, and think similarly to humans. This technology has evolved considerably over the years, and still has a long way to go.

AI is used in a wide range of applications and has many advantages but also comes with concerns and limitations. Some of the key ethics and biases in AI are discussed below.

Bias is Artificial Intelligence

Usually machines shouldn't be biased, as they don't have experiences or memories. But that's not the case with AI-based machines since they learn from data. Some of the common AI biases are −

Artificial Intelligence learns from the data, and if that data is incorrect or misleading, the outputs of the algorithms will also be inaccurate.

• Algorithm Bias − If the algorithm fed to the system itself is faulty, it affects the outcome of the algorithm as well.
• Sample Bias − If the dataset you picked is irrelevant and not accurate, the error will reflect on the results.
• Prejudice Bias − This is similar to sample bias, prejudice bias uses data that is influenced by social biases like prejudice and discrimination.
• Measurement Bias − This bias occurs when data is incorrectly collected, measured, and integrated.
• Exclusion Bias − This bias occurs when an important data point is left out of a dataset, often because of human negligence, which could intentional (not knowing the significance) or by mistake.
• Selection Bias − This bias occurs when the data used to train the algorithm doesn't reflect the real-world distribution.

Prevention of Bias

Bias often causes inequality and regulatory challenges, To address these issues, organizations should take certain measures to develop ethical practices. Some of the key measures to prevent bias are −

• Most biases occur due to small or limited datasets. To avoid this, collect as much data as possible from multiple sources to diversify the dataset.
• Run multiple tests during the early stages of testing to check for biases and correct them.
• Continuously check for the data quality as time passes.

Ethics in Artificial Intelligence

Ethics in AI is a set of principles and considerations which enhance the development, deployment, and impact of AI technologies. The key ethical issues in AI include −

• Privacy − We feed the machines with the personal details of people to help it think and act like humans. But how do we know that the details are secure and private? Data privacy is one of the crucial concerns in the development and use of AI.
• Transparency − Transparency in AI ethics refers to the practice of making AI systems and their operations understandable to users, and the way to do this is disclosure.
• Accountability − Establishing a clear note of accountability is important, especially while operating in critical areas like healthcare or law enforcement. This allows users to learn who would be responsible for the outcomes of AI systems.
• Human Dependence − AI systems are capable of automating some tasks that humans previously used to perform, especially tasks with data. However, as AI will never take the responsibility and accountability, it is important that the task of decision-making should be denied.
• Social Impact − The use of AI over employment, social interactions, and power dynamics must be carefully considered to promote positive outcomes.