Explain Ability, Fairness and Trust in Data Systems and Analysis

From credit scoring algorithms to face recognition systems, data-driven technologies are already impacting our lives and choices. To guarantee that new technologies function for the advantage of everyone, rather than perpetuating prejudices and discrimination, we have to address the ability, fairness, and trustworthiness of data systems and analysis. In literature, ability of data systems and analysis is defines as the capability of data systems to capture, store, retrieve, and manipulate data in large databases. Fairness of data systems means making sure that the usage of data and analytics does not result in biased or discriminating results for individuals or organizations. Trust is an essential factor in the effectiveness and acceptability of data systems and analysis. Without trust, people may be unwilling to utilize or disclose their data with such systems, hampering their ability to give useful insights and take advantages of data systems. To analyze, the ability, fairness, and trust in data systems and analysis, researchers have used secondary, qualitative data. Researchers have used scholarly publications, research papers, and reports taken from credible and peer-reviewed research journals. Using the data, researchers came to the result that data systems analysis have enhanced decision making process and helped us to improve our productivity in daily-life activities, through its capabilities of capturing, stories, retrieving and manipulating data. Yet, there are issues regarding the fairness of data systems. While progress achieved in tackling fairness issues, trust issues concerning data systems and analysis exist owing to data breaches suffered by huge databases globally. To conclude, in order to create trust in data systems and analysis, firms need to concentrate on security, data quality, transparency and accountability of data. This requires implementing strong access limits, leveraging high-quality data sources, being transparent and honest about data collecting and analysis, and investing in training and development programmes.