We are considering getting a tool which runs linear regression without checking the assumptions of the method (the tool is automated, it generates linear regression based on data you put in including generating the report). The problem is that the tool does not check any regression assumptions (which is not surprising given that its not a Dason level AI). 
I raised concerns about that, but I am not sure myself. I know the theory behind violations of the assumptions such as the Gauss Markov assumptions, but how serious can they be in practice (especially if you have hundreds of cases). And do any of the violations other than independence (which can't easily be tested easily for anyhow) actually bias the results? The only violations I know that do that would be some forms of time series and omitted variable bias.
I raised concerns about that, but I am not sure myself. I know the theory behind violations of the assumptions such as the Gauss Markov assumptions, but how serious can they be in practice (especially if you have hundreds of cases). And do any of the violations other than independence (which can't easily be tested easily for anyhow) actually bias the results? The only violations I know that do that would be some forms of time series and omitted variable bias.