BLIP is a system that extracts, summarizes, and categorizes undesirable consequences of digital technologies from online articles, and presents them in an interactive, web-based interface. The system was tested with 15 researchers in various computer science disciplines, and found to substantially increase the number and diversity of undesirable consequences they could list compared to relying on prior knowledge or searching online. BLIP also helped them identify consequences relevant to their ongoing projects, made them aware of consequences they "had never considered," and inspired them to reflect on their own experiences with technology. The system uses natural language processing (NLP) techniques to automatically extract real-world undesirable consequences from online articles, summarize and categorize them based on the aspect of life they affect, and present them in an interactive interface. The system was evaluated in two user studies, and found to be useful and actionable in the context of specific projects that participants work on across CS subdisciplines. BLIP contributes empirical evidence that a catalog of undesirable consequences supports CS researchers in considering more, and more diverse, undesirable consequences than if they rely on their prior knowledge or an online search. It also provides an open-source, web-based system that collects, summarizes, and categorizes undesirable consequences. The system uses an information distillation pipeline that leverages NLP techniques to efficiently establish a self-updating catalog of undesirable consequences. The system was evaluated on three technology domains: social media, virtual reality, and voice assistants. The results showed that BLIP's content curation pipeline was effective in extracting and summarizing undesirable consequences from articles. The system was also found to be useful for researchers in identifying and categorizing undesirable consequences. The system allows users to import articles, and automatically checks for new articles in the three domains on a weekly basis and adds the discussed undesirable consequences. The system was evaluated in two studies, and found to be useful for researchers in discovering and categorizing undesirable consequences. The system was found to be effective in helping researchers gain awareness of potential impacts within their general CS subarea. The system was also found to be useful for researchers in identifying and categorizing undesirable consequences. The system was found to be effective in helping researchers gain awareness of potential impacts within their general CS subarea.BLIP is a system that extracts, summarizes, and categorizes undesirable consequences of digital technologies from online articles, and presents them in an interactive, web-based interface. The system was tested with 15 researchers in various computer science disciplines, and found to substantially increase the number and diversity of undesirable consequences they could list compared to relying on prior knowledge or searching online. BLIP also helped them identify consequences relevant to their ongoing projects, made them aware of consequences they "had never considered," and inspired them to reflect on their own experiences with technology. The system uses natural language processing (NLP) techniques to automatically extract real-world undesirable consequences from online articles, summarize and categorize them based on the aspect of life they affect, and present them in an interactive interface. The system was evaluated in two user studies, and found to be useful and actionable in the context of specific projects that participants work on across CS subdisciplines. BLIP contributes empirical evidence that a catalog of undesirable consequences supports CS researchers in considering more, and more diverse, undesirable consequences than if they rely on their prior knowledge or an online search. It also provides an open-source, web-based system that collects, summarizes, and categorizes undesirable consequences. The system uses an information distillation pipeline that leverages NLP techniques to efficiently establish a self-updating catalog of undesirable consequences. The system was evaluated on three technology domains: social media, virtual reality, and voice assistants. The results showed that BLIP's content curation pipeline was effective in extracting and summarizing undesirable consequences from articles. The system was also found to be useful for researchers in identifying and categorizing undesirable consequences. The system allows users to import articles, and automatically checks for new articles in the three domains on a weekly basis and adds the discussed undesirable consequences. The system was evaluated in two studies, and found to be useful for researchers in discovering and categorizing undesirable consequences. The system was found to be effective in helping researchers gain awareness of potential impacts within their general CS subarea. The system was also found to be useful for researchers in identifying and categorizing undesirable consequences. The system was found to be effective in helping researchers gain awareness of potential impacts within their general CS subarea.