Asset Management in Machine Learning: State-of-research and State-of-practice
Machine learning components are essential for today's software systems, causing a need to
adapt traditional software engineering practices when developing machine-learning-based …
adapt traditional software engineering practices when developing machine-learning-based …
A meta-summary of challenges in building products with ml components–collecting experiences from 4758+ practitioners
Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …
software-engineering challenges and exacerbates existing ones. Many researchers have …
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
N Sambasivan, S Kapania, H Highfill… - proceedings of the …, 2021 - dl.acm.org
AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …
Data quality carries an elevated significance in high-stakes AI due to its heightened …
Investigating how practitioners use human-ai guidelines: A case study on the people+ ai guidebook
Artificial intelligence (AI) presents new challenges for the user experience (UX) of products
and services. Recently, practitioner-facing resources and design guidelines have become …
and services. Recently, practitioner-facing resources and design guidelines have become …
Studying up machine learning data: Why talk about bias when we mean power?
Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …
Collaboration challenges in building ml-enabled systems: Communication, documentation, engineering, and process
The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …
need for software engineers to collaborate with data scientists and other specialists. While …
Exploring how machine learning practitioners (try to) use fairness toolkits
Recent years have seen the development of many open-source ML fairness toolkits aimed
at helping ML practitioners assess and address unfairness in their systems. However, there …
at helping ML practitioners assess and address unfairness in their systems. However, there …
How ai developers overcome communication challenges in a multidisciplinary team: A case study
The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …
collaborating with the AI developers, an umbrella term we use to include data scientists and …
Metaagents: Simulating interactions of human behaviors for llm-based task-oriented coordination via collaborative generative agents
Significant advancements have occurred in the application of Large Language Models
(LLMs) for various tasks and social simulations. Despite this, their capacities to coordinate …
(LLMs) for various tasks and social simulations. Despite this, their capacities to coordinate …
Human factors in model interpretability: Industry practices, challenges, and needs
As the use of machine learning (ML) models in product development and data-driven
decision-making processes became pervasive in many domains, people's focus on building …
decision-making processes became pervasive in many domains, people's focus on building …