AI / ML
subtraction to make predictions , but the problem came from the inputs , publicly available writings and articles written by humans . From the shocking discovery of gender and racial bias in word2vec , a whole field of research was sparked – but machine learning researchers are continuing to have problems with written language .”
bias has been observed . Word2vec gives developers the possibility to translate words into vectors and to do basic maths on words and topics . A concrete example : using word2vec in 2017 ( 1,2 ) you could predict words with maths , such as “ King - Man + Woman ” and obtain the result “ Queen ”, but also “ Doctor - Man + Woman ” and get “ Nurse ”.”
She adds : “ This is when the machine learning community started realising how word embeddings could make mistakes . The application of maths was the same because it was made of addition and
Overcoming bias in NLP and speech recognition The first step in overcoming the bias in NLP and speech recognition is recognising that the bias itself exists . Despite there being many approaches , tackling bias comes with no quick fix and technologists need to take a delicate yet thorough approach .
“ Design techniques such as Humanin-the-loop , participatory design and multi-stakeholder involvement will help in the early stages of an AI ’ s development . When choosing the training data sets , question whether they ’ re suitable for the outcome function , applications , and domains , and it can help reduce statistical bias . There are several techniques to achieve a more balanced statistical representation in datasets , such as various class imbalance measures to detect , as well as the mitigation of bias in datasets ,” comments Fehling .
“ Social and cultural factors must form part of the analysis , as they often cannot be directly captured by the aforementioned techniques . Such issues of ‘ flattening ’ the societal and behavioural factors inherent in the datasets themselves is often overlooked , yet results in a problematic effect ,” he continues .
Adding to this , Gosnell explains : “ Every machine learning project must use methods to verify and measure the presence of bias in its systems because there are myriad ways that gender , racial , social , and other biases can exist within a system .” aimagazine . com 45