BIAS

What is AI bias?

Bias is an irregularity in the outcome of machine learning algorithms, due to the discriminatory assumptions formed during the algorithm development process. This could be due to a narrow training set of data or a partisan algorithm.

How does AI bias happen?

We often look for causes of AI bias in nonobjective training data, but the reality is more modest: bias usually enroaches a system long before the data is gathered as well as at multiple other phases of the deep-learning procedure. Here are a few key focus areas where AI Bias is spawned.

Framing the problem

The first step in creating a deep-learning model is to determine the outcome and follow the programming of the algorithm accordingly. The first step in this process would be to translate a real-world problem so the system can understand it. So a program needs to be written to ask and answer the right questions which the company decides on extensively. The most common root cause of bias is from the problem that is framed, one that is not defined accurately enough to compute an unbiased answer.

Collecting the data

There are two main scenarios where bias is indicated in training data: either the collected data is unrealistic, or it is already representative of existing preconceptions. Variations in data is key to getting a diversely capable system, feeding a system historical data which is typically affected by some bias is not a good source for data, and neither are subjective data fields, which typically have a narrow dataset of limited attributes.

Preparing the data

Finally, it is likely that bias is introduced in the data preparation stage, which revolves around determining which attributes are to be considered by the algorithm. In the case of modelling, choosing which attributes to regard or disregard can greatly impact a model’s decision accuracy. Although the impact on accuracy can be measured easily, the bias cannot, so we end up having introduced bias into the model.

What are the types of AI bias?

AI systems have biases due to two reasons:

• Cognitive biases: These are errors caused by the thought process behind individuals’ decisions and judgments. This type of bias arises due to the oversimplification of variables translated from the real world into computable data by AI experts.
• Lack of complete data: Incomplete data is not representative of the complete story behind the data attributes picked and therefore it may contain bias.

Will AI ever be completely unbiased?

All AI systems are as robust as the data they are provided with. By cleaning your training dataset from deliberate or irreflective inferences about race, gender, or other ideological concepts, an AI system that makes unbiased data-driven decisions can be built.

However, in the real-world application of such a model, we don’t expect it to ever be completely unbiased due to the data input into the system, and this data is created by people giving room for numerous human biases to occur.

The positive way to reduce AI bias is by minimizing AI tendencies by testing data more correctly for select attributes and developing AI systems with algorithms with more responsible AI principles in mind.

How to fix biases in AI?

In the first step, making sure the data set is complete is crucial, since most AI biases only happen due to the prejudices of human origin and focusing on removing these prejudices from the data set can greatly minimize AI Bias. Unfortunately, it is not as straightforward as it appears to be.

A shut-down approach to such issues is completely breaking off classifications (such as sex or race) in data and removing the labels that could potentially cause bias. But this approach usually does not work since removed labels may adversely affect the understanding of the model and your results and make the accuracy of the model worse.

Since there are no speedy solutions to clearing biases here are a few high-level suggestions highlighting the best practices to minimise AI bias:

Steps to fixing bias in AI systems:

Analyzing the algorithm and data to evaluate where the risk of bias is high. For instance:

• • Inspect the training dataset to evaluate whether it is representative of the larger data collection to prevent common biases like sampling bias.
  • Conduct quality analysis including assessment of model metrics for distinct groups in the dataset. This verifies if the model interpretation is equivalent in the subsets of data.
  • Monitor the model over multiple cycles against biases. The result of algorithms can vary as the model learns or if training data changes.

Develop a debiasing strategy as a part of the overall AI strategy that includes a practicable set of technical, operational and organizational actions:

• • The technical strategy implicates tools that can help you identify potential sources of bias and indicate the traits in the data that impact the accuracy of the model
  • Operational strategies incorporate improving data collection processes by using internal “red teams” and third-party auditors.
  • Organizational strategy includes inducting a workplace where metrics and processes are transparently exemplified

Rectify human-involved processes as you identify tendencies. Through conditioning, operation strategy and cultural changes, companies can plan better processes to reduce bias.

Determine use cases ready for automated decision-making and scenarios where human involvement is necessary.

Enable a multidisciplinary approach. Eradicating bias is a multidisciplinary strategy that allows ethicists, social scientists, and experts with the best understanding of the nuances in the process.

Diversify your organisation. Diversity in the AI community facilitates the easier identification of biases.

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