bienvenide a mi espacio

Last semester I served as a Research mentor in a mentorship program by Frontera Devs , and got the chance to mentor an undergraduate student one-on-one about academic research. At first I was a bit nervous because it was my first time doing something like that; I’d never formally taught or mentored anyone before, let alone “teach” about conducting research, but as soon as I read my mentee’s application (all mentees were asked to provide a brief description of what they wanted to learn during the program) all of my fears were replaced with excitement. My mentee, N, asked questions that many people, especially students, have about academic research: What is it? How do I get into it? What can I research? And where do I start? Reading her application took me back to when I first joined a research lab: Back when I was still figuring out what I wanted, I had no idea what I was getting into, and didn’t even know what to expect from my supervisor. At the time I was just looking to gain career ...

Cuando empecé a escribir esta entrada a finales del 2019 estaba sonando mucho el caso de la "Apple Card" (una tarjeta de crédito desarrollada por Goldman Sachs para Apple) y de que supuestamente era un producto sexista (spoiler: sí es), y como desde hace rato tenía ganas de escribir algo sobre algoritmos sesgados dije AHORA ES CUANDO. * No voy a hablar sobre el caso de Apple sino de algoritmos sesgados en general, pero si les interesa saber la noticia se las dejo al final en los materiales referenciados.  Con los recientes avances tecnológicos y lo que vemos en los medios, mucha/os creemos que la automatización de ciertos procesos o el relegarle la toma de decisiones sobre ciertas cuestiones a la ~inteligencia artificial~ es 100% confiable y seguro. Tenemos la idea de que los programas de computadora son imparciales (porque ESDEQUE LOS ALGORITMOS SE ALIMENTAN DE DATOS ESTADÍSTICOS, como si las estadísticas no pudieran ser también sesgadas) y que por eso hay que creerles ...