Ethics and the Evolution of a Practice Profession
In the last few posts, we’ve come to see what ethics are and how we use them personally on the job. Great, we think, all this does is get me primed to be fired. And, it sure feels like it. We’ve started looking at our work and wondering what we should do and what controls we have. And, we also start to realize that, perhaps, we’re a bit powerless if we want to keep getting paid.
In part 3, we start to address this. When doctors do what they do, they don’t do their work in isolation. Sure, they may be employees of a health system, but they also roll up to professional associations. And that is what we explore today. (Side note, I’ll be doing a session on Data Ethics at the Tableau Conference as well.)
When the world was small and data didn’t have sizes like t-shirts, we analysts saw a small share of human behavior in very limited snippets. We mostly just made quarterly reports and a few of the unlucky ones did this monthly or weekly. At some point, we started to get more data from a greater variety of places and could start finding patterns about people. While interesting, it’s led to some very, very, very noticeable conundrums.
Such as what right we have to collect this data…
Or to use this data…
Or sell this data…
We realize the need to talk about data – after all, it’s grown quite large and taken on a life of its own. This isn’t new – every generation fears its latest latest technology and struggles to find the balance. Often, a big event forces the conversation. We’ve seen this with nuclear ethics – the search for one thing led to another until we had the power to destroy ourselves. In truth, our sense of discovery seems interlinked with the near-edge of destruction.
To talk, we need to define what we are. We need to unify, to take a bunch of strands we’ve allowed to lay apart and tie them together as a profession. Ethics by themselves are not enough – not without standards set by a group, not without people bringing forth sticky issues, and not without legislation. You see, that last part normalizes ethical decision-making and protects it.
ASL Interpreting as a Model
While language transfer has existed since humankind spoke more than one language, professional interpreting is relatively new in the United States for local services. American Sign Language interpreters first began formalizing the field in 1964. Meeting at Ball State University for a training session, a group of ASL interpreters decided to create a registry, which would grow to become the Registry of Interpreters for the Deaf, or the RID. In the years since, the RID grew from a roster of interpreters to a powerful certifying body, lobbying agency and professional association. Laws such as the Rehabilitation Act of 1973 and the Americans with Disabilities Act helped make regular use of professional ASL interpreters widespread.
We see a few steps in this:
- Identifying
- Defining
- Normalizing/Standardizing
- Certifying
- Regulating
Professional organizations often start with training. They recognize a need to learn skills. With this, they identify their members. They begin to define what the industry is and isn’t. They normalize on certain behaviors, often through Standards of Practices. From there, this standardization flows through new trainings and certifications. The members are governed through certification, but professional bodies use this certification to pass laws to meet that higher standard. While RID was founded in 1964, it wasn’t until the last 10-15 years that the US saw an increase in states requiring certification to interpret most encounters.
A Paradigm Shift
As data workers, we’ve tended to isolate based on tools or skills. While the tool affects certain parts of our work, we’re far more likely to discuss chart choices than the impact of our analysis on human beings. Some of this comes from the history of being isolated, but also from a lack of unification and formalization. We’ve also historically centered most of our training on how to use the tool and less on the softer, more abstract parts of our work, such as bias in data collection.
One of the more challenging parts comes from the modern nature of how we treat employment. In mirror of assembly line work, we often parts and piece jobs to only handle a small share of a larger piece. We may only work on segments of the analysis, so one person may do the ETL, another the clustering, and a third person may make the dashboard. Each person may have very limited contact with the other and no exposure to the elements being analyzed. Without an understanding of the human factor and the narrative of what’s being evaluated, we’re so removed from the whole picture that we miss the effects of our actions.
When doctors work in teams, such as for an operation, they know how their role supports the others. A failure in anesthesiology means the patient may struggle to breathe, forcing the whole team to respond to an emergency, rather than complete the intended procedure. They communicate and plan together.
The pace of innovation has also made this harder, not easier. Am I a data scientist, an analyst, or a developer? Without a common way to identify myself, I’m more likely to limit my scope and define it too rigidly. Every so many years, I may see my job differently, depending on what software I’m using and what skills get listed. We’ve only just started to feel the discomfort of working in data.
There are steps being taken to work on this. Some places are starting to put together Codes of Ethics or starting to create registries. ASL interpreting experienced this as well. Within the US, both the RID and the National Association of the Deaf (NAD) spent many years certifying interpreters before coming together in the early 2000’s. NAD started certifying because there wasn’t enough representation of key stakeholders. It took well over 30 years to come to an agreeable solution. What’s important is creating the groups, ensuring a clear vision, and not maligning key stakeholders.
We’re in the midst of a shift – from a job to a profession, from hands that do to moving to a unified team that must think and work together, and from isolation to unification. Let’s talk about it.
