algorithmic accountability in healthcare

#digitalethics2024, #keyConcepts

Algorithmic Accountability in Healthcare

Definition Algorithmic accountability in healthcare refers to the
principle that, within healthcare situations, mechanisms should be in
place to ensure that AI systems operate transparently, ethically, and
are held responsible or accountable for their outputs. This concept is
closely related to the concept of transparency, which focuses mainly on
the openness and understandability of AI processes and decisions to
scrutiny. It is also related to the concept of privacy, which describes
the protection of patients' data from unauthorized access. Algorithmic
accountability covers both of these concepts, amongst also an array of
other things which in combination form good accountability.

Implications of commitment to algorithmic accountability in healthcare
Committing to algorithmic accountability in healthcare means that we
have to ensure that AI systems do not only uphold the best standards of
accuracy and efficiency, but also that they adhere to the ethical
guidelines we have made to protect patients and clinicians alike. This
commitment places a significant emphasis on ethical ways of designing,
implementing, and evaluating AI technology continuously. The most
important requirements that are raised by this concept of accountability
include the development of audit trails, which track the decision-making
processes and the implementation of corrective measures when we observe
errors. For this, we also need to establish clear responsibilities for
outcomes. The commitment also needs necessitates that AI systems are
designed in a way that they are capable of explaining their decisions in
an understandable manner to end-users, so as to enhance trust and
informed consent.

Societal transformations required for addressing concerns raised by
algorithmic accountability in healthcare To address the concerns related
to algorithmic accountability in healthcare we are required to have
comprehensive cultural, educational, institutional and societal
transformations. Culturally, we need to see a shift towards prioritizing
ethical considerations in the deployment of AI technologies.
Educationally, it involves training healthcare professionals and AI
developers to cooperate and to think about the ethical implications of
AI, ensuring that they are equipped to handle these tools with certain
responsibility. Institutionally, healthcare facilities and AI developers
need to have robust regulatory frameworks. These shoul©d enforce
accountability standards. Societally, public awareness campaigns may be
able to inform patients or the community as a whole about the complex
ethical role AI has in healthcare, considering the benefits as well as
the risks. A holistic approach like this ensures that all the people
involved are engaged in the integration of AI in a responsible manner
into healthcare systems. This promotes an environment where technology
serves the public without compromising ethical standards.