The label “Data Ethics” has emerged recently as a rubric to bring together ethical concerns arising from AI and big data. These include ethical issues that have been with us for quite some time, for example, information privacy. New issues have gained centrality. These include:
- Fairness in decision making: Machine learning algorithms have shown racial and gender bias in decision making in areas such as employment, housing, etc.
- Due process – Perhaps a subcategory of fairness, due process questions arise in the areas related to criminal justice with for example predictive policing.
- Risk and responsibility – AI raises new questions about accountability for harms caused by computer systems. How is responsibility located when bad things are caused by autonomous agents such as self-driving cars or drone? Transparency in decision making and operations is part of the concern and the motivation behind explainable AI.
- Public good – The idea that AI has great potential to advance the public good; NGOs, the UN, big tech companies such as Microsoft are exploring how AI enhances public safety and health. There is a worry that the misuse of AI technologies will inhibit the potentially beneficial uses. Ethical data management is an enabler of positive contributions. So data ethics is about balancing harms and risks.
Data ethics is a topic of increasing commentary and publication. This page will track new developments and post commentary on the topic in the coming months..
Knowledge management is an area of research in the fields of computer science/information technology, business management, and the social sciences. It is concerned with managing the creation, capture, distribution, application, and retention of knowledge produced or acquired within or by the firm through its internal operations and external relations with suppliers, clients and other parties.
In the early 2000s, knowledge management (KM) achieved buzzword status, finding its way into the marketing materials of a large number of software providers. Today, some of the buzz has subsided, but the aims and objectives of KM are still part of many software development initiatives, sometimes embedded implicitly in programs and projects, sometimes explicitly. Position titles also continue to reflect an ongoing interest in KM, even if chief knowledge officer positions have not proliferated.
Whether KM lives on with fanfare or works quietly in the background, KM objectives will be realized to various degrees in the future. Since KM will have an impact on the working lives of many people, particularly “knowledge workers” as they are often called, it is important to explore the ethical implications of KM. Is KM a threat to the financial and intellectual well being of knowledge workers, or will it enrich them intellectually and financially? For some, KM promises to carry on the process of deskilling and devaluing workers by offloading thought and judgment and converting human knowledge into machine based intellectual property. For others KM provides an opportunity to accelerate learning, forester collaboration, and enhance the stature of knowledge workers. This issue will be explored here.