How do you prevent bias in your data models?

Ensure you are using a collaborative dataops/cross functional approach and engaging with various business, risk, privacy, ethics and technology teams to understand any potential risks and issues with definitions, data and approaches you are taking. If there are going to be interactions / decisions made regarding customers, partners or communities ensure a privacy/ethics impact assessment is done.

Preventing bias in data models is a challenge in itself, one need to identify and observe the real problem which would help you to build a tentative explaination using context of the problem by defining the purpose of data model. This would ensure that the data collected is relevant and organised. The collected data shall be processed in such a way that one could arrive at a meaningful interpretation and on the basis of it relation is established between observed data and identified problem.

I like to keep it simple. Clustering data sets can help grouping similar data for potential bias. Oversampling underrepresented groups can help overcome data bias. Of course this means that you have identified highly correlated variables.

Preventing bias in data models involves several steps: 1. Gather a representative and diverse dataset, ensuring it covers different demographics and scenarios. 2. Analyze the data for potential biases, including underrepresented groups or skewed distributions. 3. Cleanse the data, correct labeling errors, and balance class representation if needed. 4. Choose algorithms designed to reduce bias, or adjust existing algorithms to incorporate fairness constraints. 5. Select appropriate evaluation metrics that account for fairness and do not disproportionately favor one group. 6. Continuously monitor and audit the model's performance for bias, making necessary adjustments over time.

I think it starts from adequacy of instruments used in gathering the data. Other contributions are very important in the process.

Ensure you are leaveraging any data quality work performed over your data sets to understand any potential gaps, duplication or issues that may introduce bias into your model. I.e. selling to a certain demographic, but you have significant gaps in your data (e.g. your postcode data is mostly incomplete) will drive negative outcomes and set yourself up for failure.

There are several statistical techniques to be applied in the analysis of the data, which can help you identify correlations between variables, complete the missing data, and any other treatments to improve the databases that will be used in the construction of the models. There are many analytics software, both paid and open source, that can support you in this task.

This is really part of data management/data governance, we will reduce so many effort or wasting of time when we can do data discovery and data profiling in this part to make sure that we are in best place and best part when we want to get the value from data.

To apply appropriate data preprocessing and analysis methods to produce reliable data for analysis, you can follow these steps: - Data cleaning and preparation: This is the first step in data preprocessing, identifying missing / incorrect values, outliers, and other inconsistencies. - Categorical data processing: Categorical data is non-numeric data that represents categories or groups. - Variable transformation: scaling and normalizing the data - Discretization: converting continuous variables into discrete ones. - Feature extraction: selecting relevant features from the dataset - Feature engineering: creating new features from existing ones. - Data integration and preparation for modeling: merging datasets and taking imbalanced data.

Agree about data cleansing, the best part from this processing is we can create the actual data that we want so the value in the table/object will be detail and more valuable.

The purpose of the analysis should always be in consideration before any of the data handling is done. Because it might infuse more problems in the process and reduce data integrity.

Subjective bias in data visualization should be taken into account as well. For example, if you produce a frequency chart with two colored bars for a boolean variable (true | false), consider using a judgmental color scheme (e.g. green | red) if true is objectively better than false, and a non-judgmental one (e.g. purple | orange) otherwise.

It is also important that this communication is very well drafted. Applying storytelling techniques and using presentation tools will generate more empathy and interest from stakeholders for the content presented.

That PIA/ EIA you did earlier? This is where it pays off for all the paperwork you had to do. Caveats, assumptions and limitations MUST be included with any analysis provided. To manage the risk of unconcious bias consider increase use of peer-review and management reviews and sign offs as controls to mitigate risks and also ensure your messaging is polished up.

Presenting data to stakeholder or audience in cross function department often create some miss understood. So, we need to make full documentation, full explanation about the definition about the content clearly. Consider of that, we need to make a simple presenting data and objective presentation that give them about the pros and cons also.

Remembering that the results is from the data gathered from a sample and the frequency of change in human depending on what we measure should guide findings report writing not to sound final.

Choice of Model is key. Bias typically emanates from model being simpler or model assumptions and underlying theory being consistently different from the problem/data at hand. For instance if one were to learn a time series of stock market variation a model that has limited memory could get us better results than a model trained on on the entire stock data set time series. In the case of AI in Natural Language Processing ( NLP) biases can arise due to use of pre-trained embeddings that are constructed on a corpus different from the corpus one uses. In this situation the bias can be reduced by deploying AI models that use the pre trained embeddings as a initial guess and the AI model fine tune towards ground truth through training.

For those Environmental Science inclined, reference to USEPA Seven Step process for developing Data Quality Objectives (DQOs) is a great starting point.

We need to give some other method that also applied for the case of data modelling following the pros and cons so they will understand more before make a decision.

We need to take the process in holistic form instead of seeing them as separate entities. We should start thinking of questions to answer at the beginning of the study which will guide data collection, sample to focus on and what make up representativeness or sufficiency of sample for the study. Having this will help to think about necessary assumptions and other important things which should be put into consideration.