Data Biases and Generative AI: A Practitioner Discussion

Introduction

This is May from the GLB Division Lakehouse Department.

We will share "Data Biases and Generative AI: A Practitioner Discussion" on data biases and generative AI based on reports from members participating in the local Data + AI SUMMIT2023 (DAIS). The session emphasized the importance of diversity and the role of women in data and AI. Furthermore, discussions were held on the minimization of data bias, the evaluation of evaluation indicators, and the development of generative AI, including diversity and ethnicity.

Data and AI careers and the importance of diversity

First, the presenters introduced themselves and asked questions about their careers in data and AI. Based on their experience in working with data analysis and AI technology, they provided important points for working in the fields of data and AI, as well as advice on career development.

There was also discussion on the role and diversity of women in the data field. The presenters said that more women working in the fields of data and AI will create more diverse perspectives and foster innovation. He also emphasized that Databricks values ​​diversity and introduced the company's efforts.

Minimize data bias and evaluate metrics

Data bias is a phenomenon in which AI cannot make accurate predictions and judgments due to bias in the data set. In this presentation, discussions were held on issues related to minimizing data bias, controlling data, and evaluating evaluation indicators.

Here are some tips for minimizing data bias:

1. Be aware of diversity when collecting data
2. Quantitatively Assess Dataset Bias
3. Properly preprocess and cleanse data
4. Select model metrics and understand the impact of bias

In addition, the following viewpoints are important when selecting evaluation indicators.

1. Choose a metric that fits the purpose of your model
2. Evaluate by combining multiple indicators
3. Consider interpretability of metrics

Discussion of generative AI including diversity and ethnicity

Generative AI is the technology by which artificial intelligence generates new data and information. In this talk, a discussion was held on generative AI, including diversity and ethnicity.

In order to develop generative AI including diversity and ethnicity, the following points can be mentioned.

1. Include data with diverse attributes in the dataset
2. Design the generative AI algorithm with diversity in mind
3. Evaluate bias in generated data and make improvements

Through such efforts, it is expected that the development of generative AI, including diversity and ethnicity, will be promoted.

Summary

Through practical discussions on data biases and generative AI, the importance of diversity and the role of women in data and AI was emphasized. The opinion was expressed that increasing the number of women working in the data field will create more diverse perspectives and help solve the problem of data bias. In the future, the importance of human intervention and education will be required in the use of data bias countermeasures and generative AI.

Conclusion

This content based on reports from members on site participating in DAIS sessions. During the DAIS period, articles related to the sessions will be posted on the special site below, so please take a look.

Translated by Johann

www.ap-com.co.jp

Thank you for your continued support!