proposal

dev/jcon/proposal.md

@@ -8,11 +8,11 @@ In the wake of recent rapid advances in artificial intelligence (AI), it is more
 
 ### 🌐 Why supercomputing?
 
-In practice, we are often required to generate many explanations for many individuals. A firm that is using a machine learning model to screen out job applicants, for example, might be required to explain to each unsuccessful applicant why they were not admitted to the interview stage. In a different context, researchers may need to generate many explanations for [evaluation](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable/tutorials/evaluation/) and [benchmarking](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable/tutorials/benchmarking/) purposes. In both cases, the involved computational tasks can be parallelized through multi-threading or distributed computing. For this purpose, we have recently released [`TaijaParallel.jl`](https://github.com/JuliaTrustworthyAI/TaijaParallel.jl): a lightweight package that adds custom support for [parallelization](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable/tutorials/parallelization/) to [Taija](https://github.com/JuliaTrustworthyAI) packages. 
+In practice, we are often required to generate many explanations for many individuals. A firm that is using a machine learning model to screen out job applicants, for example, might be required to explain to each unsuccessful applicant why they were not admitted to the interview stage. In a different context, researchers may need to generate many explanations for [evaluation](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable/tutorials/evaluation/) and [benchmarking](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable/tutorials/benchmarking/) purposes. In both cases, the involved computational tasks can be parallelized through multi-threading or distributed computing. 
 
 ### 🤔 How supercomputing?
 
-Our goal has been to minimize the burden on users by facilitating different forms of parallelization through a simple macro. To multi-process the evaluation of a large set of `counterfactuals` using the [MPI.jl](https://juliaparallel.org/MPI.jl/latest/) backend, for example, users can proceed as follows: firstly, load the backend and instantiate the `MPIParallelizer`,
+For this purpose, we have recently released [TaijaParallel.jl](https://github.com/JuliaTrustworthyAI/TaijaParallel.jl): a lightweight package that adds custom support for [parallelization](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable/tutorials/parallelization/) to [Taija](https://github.com/JuliaTrustworthyAI) packages. Our goal has been to minimize the burden on users by facilitating different forms of parallelization through a simple macro. To multi-process the evaluation of a large set of `counterfactuals` using the [MPI.jl](https://juliaparallel.org/MPI.jl/latest/) backend, for example, users can proceed as follows: firstly, load the backend and instantiate the `MPIParallelizer`,
 
 ```julia
 using CounterfactualExplanations, TaijaParallel