Let’s have an analysis regarding the answer for Biased AI And Its Disadvantages. AI model BIASED through Biased Data can not behave normal and proper after training. Biased AI can never achieve its targets. Biased AI is AI that is programmed to lean one way or another on a specific issue or topic. This can be done consciously or unconsciously by the people who create and operate the AI.
What is Biased Data?
The data which did not collect properly with label mistakes. For example, names for a few labels in data are cats but images are not cats. Similarly, a few labels in data are shirts but their images are different.
Biased AI can learn Unhealthy Stereotypes
Learning from internet or in case of complex queries as follows;
Man: Woman as Father: Mother
Man: Woman as King: Queen
Man : Computer programmer as women: Home maker
Man and woman can equally become programmer
Please always keep in mind that Artificial Narrow Intelligence (ANI) could recognise cats (for example) from data sets containing cat and not a cat. If we ask about dog and duck it will be confused. It will learn unhealthy stereotype which will make no sense regarding our goals. See in the following figure that describes biased data.
whereas the proper data example is as:
What are the Reasons of Biased AI?
There are numerous reasons why AI may be biased. Some reasons may be intentional, while others may be unintentional. Some reasons may include:
- Pre-existing biases of the people who design and program AI algorithms
- Unconscious bias on the part of AI developers -The use of data that is biased or inaccurate
- The use of algorithms that are biased
- Poor or unfair execution of AI algorithms
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Why Biased AI Matters?
Given that AI will increasingly play a role in our lives, it is important that it is unbiased. Otherwise, it could perpetuate and even amplify existing biases in our societies. This could have far-reaching and detrimental consequences, including for economic opportunities, social cohesion and even our democracy. We therefore need to be very careful in how we design and deploy AI, and work to ensure that it is unbiased.
The followings are a few cases explaining about biased ai applications due to biased data.
If model trained with biased data regarding man and women then after training it will not make difference a woman if a man has long hair and clean shaved. Similarly the same ML model can not recognise a woman if she has cut her hair.
2. Facial recognition working better for specific ethnicity
Biased ML models can not recognise dark colour tone properly. Even a person with fair colour can be detected as criminal if it has only 30% resemblance.
3. Bank loan approvals
Biased version of machine learning model could declare a person as defaulter even he/she never get any loan. Similarly it can approve a defaulter or criminal person to get a bank loan.
4. Toxic effect of reinforcing unhealthy stereotypes
These AI outcomes are very dangerous when a biased machine learning model tell immigration officer about a person who is African American whilst his country of origin is in Asia.
How to Combating Bias?
There is no one-size-fits-all answer to this question, as the best way to combat biased AI may vary depending on the circumstances. However, some measures that can be taken to address bias in AI systems include:
- Conducting research to identify and address any potential sources of bias in the data used to train the AI system.
- Building in transparency and accountability measures so that users or other stakeholders can understand how the AI system works and identify any areas of bias.
- Using human intervention and oversight to identify and correct any instances of bias in the AI system.
- Developing clear ethical guidelines for the use of AI, which can help to prevent and address any instances of bias.
1. Technical Solutions:
- Zero out bias from data
- Use more proper data to train the ML model
- Use less biased or more bulk of quality data
2. Transparency or auditing process:
All the training should be transparent and audit able. AI engineers and ML experts must not compromise any weakness in data. A proper data can create a healthy stereotype.
3. Diverse workforce
- Nobody from employees would like to cheat the system.
- A good AI team creates zero biased applications which is ideal state i.e. the least possible biased applications could be created.
- Training of machines should be during days and nights.
Disadvantages of Biased AI
There are many potential disadvantages of biased AI.
- First, biased AI may lead to inaccurate decisions and predictions. For example, if a biased AI system is used to evaluate a job candidate, it may wrongly conclude that the candidate is not a good fit for the job. This could lead to the candidate being rejected even if they are actually a good fit, which could have a negative impact on their career.
- Second, biased AI may lead to discrimination and inequality. For example, if a biased AI system is used to evaluate job candidates, it may unfairly favour candidates from certain backgrounds or who belong to certain groups. As a result, qualified candidates from marginalized groups may be overlooked or passed over for jobs.
- Third, biased AI may increase the risk of human error. For example, if a biased AI system is used to make important decisions, such as who to hire or how to invest money, it may lead to inaccurate decisions that could have serious consequences.
- Fourth, biased AI may cause social divisiveness. For example, if a biased AI system is used to recommend products or services, it may lead to people becoming divided based on their preferences. This could lead to animosity and conflict between different groups of people.
- Finally, biased AI may have a negative impact on democracy. For example, if a biased AI system is used to make important decisions, such as who to vote for, it could lead to the disenfranchisement of certain groups of people. As a result, the democratic process could be compromised.
This analysis has discussed biased AI and its disadvantages. Biased AI is AI that has been intentionally or unintentionally trained on data that has been biased. This can lead to unfair outcomes for some people or groups of people. Some of the disadvantages of biased AI include inaccurate or unfair decision-making, decreased trust in AI, and reduced usability of AI applications. Biased AI can also lead to legal and ethical concerns. It is important to be aware of the potential for bias in AI and take steps to mitigate it where possible.