How can we prevent bias in machine learning models?
Machine Learning
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Machine learning algorithms are capable of inheriting, amplifying or creating biases against groups based on certain characteristics, such as race, gender or age. Such biases can have harmful wider consequences, such as denying access to credit, education or health care, or perpetuating stereotypes and prejudices.
Preventing bias in machine learning algorithms before and while development is a key component of addressing its larger impacts. Here is how we can begin to prevent bias in our machine learning models.?
An essential step for preventing bias in machine learning is to ensure that the data used to train, test and validate the algorithms are representative and inclusive of the relevant populations and contexts. Additionally, the data should be collected and processed in a fair and ethical manner, respecting the privacy, consent and dignity of the data subjects, and avoiding any intentional or unintentional manipulation.
And data alone is not sufficient to guarantee fairness and impartiality. The design and optimization choices made by the developers and engineers can also introduce or exacerbate bias, depending on how they define, measure and operationalize the problem or features. Therefore, developers and engineers should adopt a human-centered and value-sensitive approach that considers the needs and expectations of the end-users and the affected parties and that aligns with the ethical principles and social values of the domain and the context. They should also be aware of their own biases and seek feedback and input from diverse and multidisciplinary perspectives, such as domain experts, policy makers, ethicists and social scientists.
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Some examples of best practices for prevention can include:
- Conducting data audits and quality checks to identify and address any potential sources of bias, such as sampling errors, missing values or inconsistencies.
- Applying data augmentation and synthesis techniques to enhance the diversity and coverage of the data.
- Using fair and relevant features and labels that capture the essential and meaningful aspects of the problem, and that do not introduce or rely on any sensitive or protected attributes, such as race, gender or religion, unless explicitly justified and regulated.
- Choosing appropriate and robust loss functions and performance metrics that balance and optimize the trade-offs between different dimensions of fairness, such as equality, equity or diversity.
- Incorporating fairness constraints and objectives into the learning process, such as ensuring that the algorithms treat similar individuals or groups similarly, or that the algorithms do not disadvantage or harm any individual or group disproportionately.
- Establishing clear and consistent standards and guidelines for ethical and responsible data and algorithm design, and providing training and education for the developers and engineers on the principles and practices of fairness and diversity.
Explore more
- How To Reduce Bias in Machine Learning by Serhii Pospielov for Spiceworks
- The Risk of Machine-learning Bias (and How to Prevent It) by Chris DeBrusk for MIT Sloan Management Review
- Machines and Trust: How to Mitigate AI Bias by Michael McKenna for Toptal
This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.
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2 å¹´Good transparency in the collection and disposal of the data they use, and analysis of those processes.
AI/Data/Strategy @ Ford | MBA, MS | Empowering teams to create ethical, impactful AI solutions that drive change.
2 å¹´Taking a datacentric approach with quality data with good distribution. We have to rethink data collection at source till its used for modeling to reduce bias.Smart AI data pipelines and ingestion patterns plays a key role to achieve the above [ AI for Data to reduce bias in Data for AI ]
Operations & Data Science | Response Mgmt. | Philosophy
2 年First, by "bias", do you mean a social bias like social prejudice, or do you mean bias as in bias-variance framework? I wouldn't introduce too much room for tweaks to the data or the model – it can actually lead to overfitted, underfitted, or simply just awry results. Let the data and model speak for themselves, but have humans in the loop so that (a) the data wrangling and modeling process is clearly understood and makes sense, and (b) people can understand correlations across different features, and detect bias.
QA Manager / Altera @ Northwell Health
2 年Quite simply, you need to test for bias. That may be easier said than done but if you have data sets that would score highly as biased you can train what to avoid in the interest of objectivity. If I were a Mathematician (or Vulcan) I might propose an objective mathematical approach/solution. Bias would seem to be more of an outlier where data is concerned so statistically unbiased data should be more “normal†but unfortunately normal is not always ideal or the book “The Bell Curve†would not have been deemed so controversial. Bias can be somewhat subjective and variable as norms of a society change over time. So in conclusion I would say that within the context of current norms, bias can be tested for as an outlier. IMHO You need to know what bias looks like and test for it.
Product Management| Scrum Master| MBA| Machine Learning
2 å¹´The discussion of bias online tends to become pretty confusing pretty quickly. Let's assume we are discussing the social science concept of bias here. Before discussing how we can prevent bias in the Machine learning model, we should first identify where these biases come into the system. They may be coming from the Historical aspect or the Representation aspect. After that, we can think about measurement bias. This occurs when we measure the wrong thing, measure it in the wrong way, or incorporate the measurement into the model inappropriately. Next, in Aggregation bias, models do not aggregate data in a way that includes all of the appropriate factors or when models do not include interaction terms, nonlinearities, etc. Different types of bias require different approaches for mitigation. While gathering a more diverse dataset can address representation bias, this would not help with historical bias or measurement bias. All datasets contain bias. There is no such thing as a completely debiased dataset. One helpful resource for this is the free online book (https://fairmlbook.org/) "Fairness and Machine Learning: Limitations and Opportunities" by Solon Barocas et al.