How Might Ethical Data Principles Borrow from Social Work?

I’m in the process of co-writing a book called Data Feminism, together with Lauren Klein. Recently, I’ve had the privilege of interviewing people who are working at the cutting edge of ethical data and inclusive artificial intelligence. Two people in particular have brought our attention to the field of social work in relation to data science. In an article about data violence, Anna Lauren Hoffmann wrote that “engineers and data scientists need to listen to and engage with affected communities.” Though engineers and data scientists are typically not equipped for engaging with communities, social workers have deep expertise in that regard. She and I recently discussed the striking lack of overlap between social work and information science.

But then there are exceptions. A notable one is the work of Desmond U.Patton,PHD and William R. Frey who are working at the intersection of social work, violence prevention and artificial intelligence. They recently published the paper Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data. The paper includes a transparent statement about the values that guide their work, as well as which groups they collaborate with (community-based organizations) and which groups they do not (law enforcement). It also details some of the challenges of doing data science in collaboration with marginalized youth.

What if This Were the Data Science Code of Ethics?

In discussing where the ethical framework for their paper came from, Patton pointed me to the National Association of Social Workers Code of Ethics. As I read over the list of ethical principles, I wondered whether designers, data scientists and engineers might ever be able to so clearly and explicitly deal with issues of justice and oppression.

Let us try, for a moment, to imagine that world. Below are the ethical principles copied from the NASW site. Everywhere it said “social work” or “social worker”, I substituted “data science” and “data scientist.”

The following broad ethical principles are based on data science’s core values of service, social justice, dignity and worth of the person, importance of human relationships, integrity, and competence. These principles set forth ideals to which all data scientists should aspire.

Value: Service

Ethical Principle: Data scientists’ primary goal is to help people in need and to address social problems.

Data scientists elevate service to others above self-interest. Data scientists draw on their knowledge, values, and skills to help people in need and to address social problems. Data scientists are encouraged to volunteer some portion of their professional skills with no expectation of significant financial return (pro bono service).

Value: Social Justice

Ethical Principle: Data scientists challenge social injustice.

Data scientists pursue social change, particularly with and on behalf of vulnerable and oppressed individuals and groups of people. Data scientists’ social change efforts are focused primarily on issues of poverty, unemployment, discrimination, and other forms of social injustice. These activities seek to promote sensitivity to and knowledge about oppression and cultural and ethnic diversity. Data scientists strive to ensure access to needed information, services, and resources; equality of opportunity; and meaningful participation in decision making for all people.

Value: Dignity and Worth of the Person

Ethical Principle: Data scientists respect the inherent dignity and worth of the person.

Data scientists treat each person in a caring and respectful fashion, mindful of individual differences and cultural and ethnic diversity. Data scientists promote clients’ socially responsible self-determination. Data scientists seek to enhance clients’ capacity and opportunity to change and to address their own needs. Data scientists are cognizant of their dual responsibility to clients and to the broader society. They seek to resolve conflicts between clients’ interests and the broader society’s interests in a socially responsible manner consistent with the values, ethical principles, and ethical standards of the profession.

Value: Importance of Human Relationships

Ethical Principle: Data scientists recognize the central importance of human relationships.

Data scientists understand that relationships between and among people are an important vehicle for change. Data scientists engage people as partners in the helping process. Data scientists seek to strengthen relationships among people in a purposeful effort to promote, restore, maintain, and enhance the well-being of individuals, families, social groups, organizations, and communities.

Value: Integrity

Ethical Principle: Data scientists behave in a trustworthy manner.

Data scientists are continually aware of the profession’s mission, values, ethical principles, and ethical standards and practice in a manner consistent with them. Data scientists act honestly and responsibly and promote ethical practices on the part of the organizations with which they are affiliated.

Value: Competence

Ethical Principle: Data scientists practice within their areas of competence and develop and enhance their professional expertise.

Data scientists continually strive to increase their professional knowledge and skills and to apply them in practice. Data scientists should aspire to contribute to the knowledge base of the profession.

Source: This speculative code of ethics for data science was adapted from the National Association of Social Workers Code of Ethics.