What is data ethics ? (UGC0725019)

Data Ethics

Data science provides huge opportunities to improve private and public life, as well as our environment (consider the development of smart cities or the problems caused by carbon emissions). Unfortunately, such opportunities are also coupled to significant ethical challenges. The extensive use of increasingly more data—often personal, if not sensitive (big data)—and the growing reliance on algorithms to analyses them in order to shape choices and to make decisions (including machine learning, artificial intelligence and robotics), as well as the gradual reduction of human involvement or even oversight over many automatic processes, pose pressing issues of fairness, responsibility and respect of human rights, among others

 

• Data Science brings huge opportunities but is coupled with significant ethical challenges.
  
  

• The widespread use of data (often big data, personal, or sensitive), reliance on algorithms 

   (like AI and machine learning) to shape 

  choices, and the reduction of human oversight pose urgent issues related to fairness, 

   responsibility, and human rights.
  
  

• Data ethics is presented as a new branch of ethics designed to study and evaluate moral problems arising from data, algorithms, 

  and corresponding practices.
  

• Data ethics must be developed as a macro ethics an overall, consistent, holistic, and inclusive framework to avoid narrow,
   ad hoc approaches and maximize the social value of data science. 
   

 Defining the Field

• Data ethics builds upon the foundation of computer and information ethics.
  
  

• A key refinement is the shift in the level of abstraction (LoA) of ethical inquiries from being information-centrist to data-centrist
  
  

• The data - centrist brings into focus the moral dimensions of all kinds of data, even 

  data that doesn't directly become information but supports actions or generates behaviors
  
  

• Under the ethical challenges are mapped out along three core axes of research:

   

  1. The Ethics of Data: Focuses on ethical problems from collecting and analyzing
     large datasets,
  2.  including issues like re-identification, group privacy (e.g., discrimination like ageism or
     sexism), trust, and transparency 
     
     
  3.  The Ethics of Algorithms: Addresses issues posed by the complexity and autonomy of
     algorithms
     (including AI and machine learning), such as moral responsibility and accountability for
     unforeseen consequences. Ethical design and auditing are crucial.
     
     
  4.  The Ethics of Practices: Concerns the responsibilities and liabilities of people and
     organizations
     (like data scientists) in charge of data processes, strategies, and policies. Key topics
     include consent, user privacy, and secondary use 

  
  Why the Data - Centrist Shift Matters

   

• The change in focus reflects that it is not the specific digital technology
  (e.g., computers, tablets) but what any digital technology manipulates that is
  the correct ethical focus.
  
  

• Labels like robo-ethics or machine ethics are considered to miss the point by
  anachronistically focusing on the hardware the difficulty comes from what the hardware does with the software and the data.
  
  

• The data-centric view highlights that ethical problems like privacy, anonymity, and
  trust fundamentally concern data collection, curation, analysis, and use before they
  concern information.
  
  

• These three axes of research (data, algorithms, practices) are intertwined, not separate.
  Ethical problems are conceptual points that often require analysis across all three axes
  

• The goal of data ethics is to successfully navigate between the Scylla of social rejection (from overlooking ethical issues, like the NHS care.data programmer) and the
  Charybdis of legal prohibition (from overly rigid regulations) to maximize the ethical value of data science.
   
  So in conclusion,
   

• Developing data ethics as a macro ethics provides the necessary overall framework
  (geometry of the ethical space) to address the diverse ethical implications of data science
  within a consistent, holistic, and inclusive approach.
  
  

• Striking a robust balance between fostering data science development and ensuring the
  respect of human rights and democratic values is the demanding task of data ethics.
  
  

• Social acceptability must be a guiding principle for any data science project impacting human life.
  
  

  
  

  
  

 References : What is data ethics ?

 Cite this article (Author) : Floridi l, Taddeo M